This page provides a guide to Establishing a Connection to MongoDB in CData Cloud, as well as information on the available resources, and a reference to the available connection properties.

Connecting to MongoDB

Establishing a Connection shows how to authenticate to MongoDB and configure any necessary connection properties to create a database in CData Cloud

Accessing Data from CData Cloud Services

Accessing data from MongoDB through the available standard services and CData Cloud administration is documented in further details in the CData Cloud Documentation.

Connect to MongoDB by selecting the corresponding icon in the Database tab. Required properties are listed under Settings. The Advanced tab lists connection properties that are not typically required.

Connecting to MongoDB

Set the following connection properties to connect to a single MongoDB instance:

• Server: Set this to the name or address of the server your MongoDB instance is running on. You can specify the port here or in Port.
• Database: Set this to the database you want to read from and write to.

Connecting to MongoDB Using DNS Seed Lists

• Server: Set this to "mongodb+srv://"" + the name of the server your MongoDB instance is running on. You can specify the port here or in Port.
• Database: Set this to the database you want to read from and write to.
• DNSServer: Set this to the hostname of a DNSServer that can resolve the necessary DNS entries.

Connecting to Replica Sets

To connect to a replica set, set the following in addition to the preceding connection properties:

• ReplicaSet: Set this to a comma-separated list of secondary servers in the replica set, specified by address and port.
• SlaveOK: Set this to true if you want to read from secondary (slave) servers.
• ReadPreference: Set this to fine-tune how the Cloud reads from secondary servers.

Securing MongoDB Connections

You can set UseSSL to negotiate SSL/TLS encryption when you connect.

Authenticating MongoDB Connections

Supported AuthScheme types (MONGODB-CR,SCRAM-SHA-1,SCRAM-SHA-256,PLAIN,GSSAPI) are challenge-response authentication and LDAP.

Challenge-Response

In challenge-response authentication, the User and Password properties correspond to a username and password stored in a MongoDB database. If you want to connect to data from one database and authenticate to another database, set both Database and AuthDatabase.

LDAP

To use LDAP authentication, set AuthDatabase to "$external" and set AuthScheme to PLAIN. This value specifies the SASL PLAIN mechanism; note that this mechanism transmits credentials over plaintext, so it is not suitable for use without TLS/SSL on untrusted networks.

X.509 Certificates

Set AuthScheme to X509 to use X.509 certificate authentication.

Connecting to an Amazon DocumentDB Cluster

Before you can connect to Amazon DocumentDB, you will first need to, ensure your Amazon DocumentDB cluster and the EC2 instance containing the mongo shell are currently running.

Next, configure an SSH tunnel to the EC2 instance as follows.

1. From the AWS management console, select Services -> Database -> Amazon DocumentDB. From the DocumentDB management page, select Clusters, then click your cluster.
2. Under the Connect section, note the --host value and its port found in the sample connection string.
3. Navigate to Services -> Compute -> EC2. Select Running instances.
4. Select your instance, then click the Connect button.
5. Under the Example section, note the value identifying the instance and user, shown in the form <ami_username>@<Public DNS>
6. In your preferred SSH client, establish a connection to your EC2 instance using the Host Name from the EC2 instance's Connect page (username@publicDNS) and Port 22.
7. Provide your EC2 instance's private key file (in Putty, you will need to convert the keys from .pem to .ppk) for authentication.
8. Configure an SSH tunnel using the port and host name from the DocumentDB cluster page.
9. Establish the connection to the EC2 virtual machine.

Specify the following to connect to the DocumentDB cluster.

• Server: Set this to the machine name which is hosting the SSH tunnel.
• Port: Set this to the port the SSH tunnel is hosted on.
• User: Set this to the master username used to provision the DocumentDB cluster.
• Password: Set this to the master password set when provisioning the DocumentDB cluster.
• UseSSL: Set this to true.
• UseFindAPI Set this to true.

Connecting to CosmosDB with the MongoDB API

To obtain the connection string needed to connect to a Cosmos DB account using the MongoDB API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values.

• Server: Set this to the Host value, the FQDN of the server provisioned for your account. You can also specify the port here or in Port.
• Port: Set this to the port.
• Database: Set this to the database you want to read from and write to.
• User: Set this to the database user.
• Password: Set this to the user's password.

When you connect to Atlas, ObjectRocket, or another database-as-a-service provider, there typically are a few variations on the procedure outlined in Establishing a Connection. The following sections show how to obtain the necessary connection properties for several popular services.

Atlas

You can authenticate to MongoDB Atlas with a MongoDB user or an LDAP user. The following sections show how to map Atlas connection strings to Cloud connection properties. To obtain the Atlas connection string, follow the steps below:

1. In the Clusters view, click Connect for the cluster you want to connect to.
2. Click Connect Your Application.
3. Select either driver option to display a connection string.

Prerequisites

In addition to creating a MongoDB user and/or setting up LDAP, your Atlas project's white-list must include the IP address of the machine the Cloud is connecting from. To add an IP address to the white-list, select the Security tab in the Clusters view and then click IP Whitelist -> Add IP Address.

MonogDB User Credentials

Below is an example connection string providing a MongoDB user's credentials.

mongodb://USERNAME:[email protected]:27017,cluster0-shard-00-01.mongodb.net:27017,cluster0-shard-00-02.mongodb.net:27017/test?ssl=true&replicaSet=Cluster0-shard-0&authSource=admin

• Server: Set this to the first server in the replica set. Or, you can specify a primary or secondary server here (the Cloud will query the servers in Server and ReplicaSet to find the primary).
  

  cluster0-shard-00-00.mongodb.net

• Port: Set this to the port the server is running on (27017 is the default).
• ReplicaSet: Set this to the other servers in the replica set. Server and ReplicaSet together specify all instances in the MongoDB replica set. Specify both the server name and port in ReplicaSet.
  

  mycluster0-shard-00-01.mongodb.net:27017,mycluster0-shard-00-02.mongodb.net:27017

• SlaveOK: Set this to true to allow reading from secondary (slave) servers in the replica set.
• AuthDatabase: Set this to "admin" to connect to MongoDB Atlas. All MongoDB users for Atlas are associated with the admin database, their authentication database.
• Database: Set this to the database you want to read from and write to.

• User: Set this to the username of a MongoDB user you added to your MongoDB project.

• Password: Set this to the password of the MongoDB user.

• UseSSL: Set this to true. Atlas requires TLS/SSL.

LDAP

The following list shows the MongoDB Atlas requirements for authenticating with an LDAP user. Below is an example command to connect with the mongo client:

mongo "mongodb://cluster0-shard-00-00.mongodb.net:27017,cluster0-shard-00-01.mongodb.net:27017,cluster0-shard-00-02.mongodb.net:27017/test?ssl=true&replicaSet=Cluster0-shard-0&authSource=$external" --authenticationMechanism PLAIN --username cn=rob,cn=Users,dc=atlas-ldaps-01,dc=myteam,dc=com 

• Server: Set this to the first server in the replica set. Or, you can specify another primary or secondary server here (the Cloud will query the servers in Server and ReplicaSet to find the primary). For example:
  

  cluster0-shard-00-00.mongodb.net

• Port: Set this to the port the server is running on (27017 is the default).
• ReplicaSet: Set this to the other servers in the replica set. Server and ReplicaSet together specify all instances in the MongoDB replica set. Below is an example value:
  

  mycluster0-shard-00-01.mongodb.net:27017,mycluster0-shard-00-02.mongodb.net:27017

• SlaveOK: Set this to true to allow reading from secondary (slave) servers in the replica set.

• AuthScheme: Set AuthScheme to PLAIN in LDAP authentication.

• Database: Set this to the database you want to read from and write to.

• AuthDatabase: Set this to "$external" to authenticate with an LDAP user.

• User: Set this to the full Distinguished Name (DN) of a user in your LDAP server as the Atlas username. For example:
  

  cn=rob,cn=Users,dc=atlas-ldaps-01,dc=myteam,dc=com

• Password: Set this to the password of the LDAP user.

• UseSSL: Set this to true. Atlas requires TLS/SSL.

ObjectRocket

To connect to ObjectRocket, you authenticate with the credentials for a database user. You can obtain the necessary connection properties from the control panel: On the Instances page, select your instance and then select the Connect menu to display a MongoDB connection string.

Prerequisites

In addition to adding a user for your database, you also need to allow access to the IP address for the machine the Cloud is connecting from. You can configure this by selecting your instance on the Instances page and then clicking Add ACL.

MongoDB User

mongodb://YOUR_USERNAME:[email protected]:52826,abc123-d4-2.mongo.objectrocket.com:52826,abc123-d4-1.mongo.objectrocket.com:52826/YOUR_DATABASE_NAME?replicaSet=89c04c5db2cf403097d8f2e8ca871a1c

• Server: Set this to the first server in the replica set. Click Replica Set to obtain the server names. Or, you can specify another primary or secondary server here (the Cloud will query the servers in Server and ReplicaSet to find the primary).
  

  abc123-d4-0.mongo.objectrocket.com

• Port: Set this to the port the server is running on (27017 is the default).
• ReplicaSet: Set this to the other servers in the replica set. Server and ReplicaSet together specify all instances in the MongoDB replica set. Below is an example value:
  

  abc123-d4-2.mongo.objectrocket.com:52826,abc123-d4-1.mongo.objectrocket.com:52826

• Database: Set this to the database you want to read from and write to. Note that this is also the authentication database for the user you are connecting with; database users cannot interact with other databases outside their database in ObjectRocket.
• User: Set this to the username of a MongoDB user you defined for the Database.
• Password: Set this to the password for the database user.
• UseSSL: Set this to true to enable TLS/SSL.

General Changes

MongoDB is a schemaless, document database that provides high performance, availability, and scalability. These features are not necessarily incompatible with a standards-compliant query language like SQL-92. In this section we will show various schemes that the Cloud offers to bridge the gap with relational SQL and a document database.

Working with MongoDB Objects as Tables

The Cloud models the schemaless MongoDB objects into relational tables and translates SQL queries into MongoDB queries to get the requested data. See Query Mapping for more details on how various MongoDB operations are represented as SQL.

Discovering Schemas Automatically

The Automatic Schema Discovery scheme automatically finds the data types in a MongoDB object by scanning a configured number of rows of the object. You can use RowScanDepth, FlattenArrays, and FlattenObjects to control the relational representation of the collections in MongoDB. You can also write Free-Form Queries not tied to the schema.

The Cloud automatically infers a relational schema by inspecting a series of MongoDB documents in a collection. You can use the RowScanDepth property to define the number of documents the Cloud will scan to do so. The columns identified during the discovery process depend on the FlattenArrays and FlattenObjects properties.

Flattening Objects

If FlattenObjects is set, all nested objects will be flattened into a series of columns. For example, consider the following document:

{
  id: 12,
  name: "Lohia Manufacturers Inc.",
  address: {street: "Main Street", city: "Chapel Hill", state: "NC"},
  offices: ["Chapel Hill", "London", "New York"],
  annual_revenue: 35,600,000
}

If FlattenObjects is not set, then the address.street, address.city, and address.state columns will not be broken apart. The address column of type string will instead represent the entire object. Its value would be {street: "Main Street", city: "Chapel Hill", state: "NC"}. See JSON Functions for more details on working with JSON aggregates.

Flattening Arrays

The FlattenArrays property can be used to flatten array values into columns of their own. This is only recommended for arrays that are expected to be short, for example the coordinates below:

"coord": [ -73.856077, 40.848447 ]

It is best to leave other unbounded arrays as they are and piece out the data for them as needed using JSON Functions.

As discussed in Automatic Schema Discovery, intuited table schemas enable SQL access to unstructured MongoDB data. JSON Functions enable you to use standard JSON functions to summarize MongoDB data and extract values from any nested structures. Custom Schema Definitions enable you to define static tables and give you more granular control over the relational view of your data; for example, you can write schemas defining parent/child tables or fact/dimension tables. However, you are not limited to these schemes.

After connecting you can query any nested structure without flattening the data. Any relations that you can access with FlattenArrays and FlattenObjects can also be accessed with an ad hoc SQL query.

Let's consider an example document from the following Restaurant data set:

 
{
  "address": {
    "building": "1007",
    "coord": [
      -73.856077,
      40.848447
    ],
    "street": "Morris Park Ave",
    "zipcode": "10462"
  },
  "borough": "Bronx",
  "cuisine": "Bakery",
  "grades": [
    {
      "grade": "A",
      "score": 2,
      "date": {
        "$date": "1393804800000"
      }
    },
    {
      "date": {
        "$date": "1378857600000"
      },
      "grade": "B",
      "score": 6
    },
    {
      "score": 10,
      "date": {
        "$date": "1358985600000"
      },
      "grade": "C"
    }
  ],
  "name": "Morris Park Bake Shop",
  "restaurant_id": "30075445"
} 

SELECT [address.building], [grades.1.grade] FROM restaurants WHERE restaurant_id = '30075445'

It is possible to retrieve an array of documents as if it were a separate table. Take the following JSON structure from the restaurants collection for example:

{
  "_id" : ObjectId("568c37b748ddf53c5ed98932"),
  "address" : {
    "building" : "1007",
    "coord" : [-73.856077, 40.848447],
    "street" : "Morris Park Ave",
    "zipcode" : "10462"
  },
  "borough" : "Bronx",
  "cuisine" : "Bakery",
  "grades" : [{
      "date" : ISODate("2014-03-03T00:00:00Z"),
      "grade" : "A",
      "score" : 2
    }, {
      "date" : ISODate("2013-09-11T00:00:00Z"),
      "grade" : "A",
      "score" : 6
    }, {
      "date" : ISODate("2013-01-24T00:00:00Z"),
      "grade" : "A",
      "score" : 10
    }, {
      "date" : ISODate("2011-11-23T00:00:00Z"),
      "grade" : "A",
      "score" : 9
    }, {
      "date" : ISODate("2011-03-10T00:00:00Z"),
      "grade" : "B",
      "score" : 14
    }],
  "name" : "Morris Park Bake Shop",
  "restaurant_id" : "30075445"
}

SELECT * FROM [restaurants.grades]

You may also want to include information from the base restaurants table. You can do this with a join. Flattened arrays can only be joined with the root document. The Cloud expects the left part of the join is the array document you want to flatten vertically. Disable SupportEnhancedSQL to join nested MongoDB documents -- this type of query is supported through the MongoDB API.

SELECT [restaurants].[restaurant_id], [restaurants.grades].* FROM [restaurants.grades] JOIN [restaurants] WHERE [restaurants].name = 'Morris Park Bake Shop'

It's also possible to build queries targeting arrays within other arrays.

Consider this sample Inventory collection:

{
	"_id": {
		"$oid": "xxxxxxxxxxxxxxxxxxxxxx"
	},
	"Company Branch": "Main Branch",
	"ItemList": [
		{
			"item": "journal",
			"instock": [
				{
					"warehouse": "A",
					"qty": 15
				},
				{
					"warehouse": "B",
					"qty": 45
				}
			]
		},
		{
			"item": "paper",
			"instock": [
				{
					"warehouse": "A",
					"qty": 50
				},
				{
					"warehouse": "B",
					"qty": 5
				}
			]
		}
	]
}

Insert data into the nested arrays using the syntax of <parent array>.<index>.<child array>, as follows:

INSERT INTO [Inventory.ItemList] (p_id, item, [instock.0.warehouse], [instock.0.qty], [instock.0.price]) VALUES ('xxxxxxxxxxxxxxxxxxxxxx', 'NoteBook', 'B', 20, '5$')

The Inventory collection after executing the INSERT statement:

{
	"_id": {
		"$oid": "xxxxxxxxxxxxxxxxxxxxxx"
	},
	"Company Branch": "Main Branch",
	"ItemList": [
		{
			"item": "journal",
			"instock": [
				{
					"warehouse": "A",
					"qty": 15
				},
				{
					"warehouse": "B",
					"qty": 45
				}
			]
		},
		{
			"item": "paper",
			"instock": [
				{
					"warehouse": "A",
					"qty": 50
				},
				{
					"warehouse": "B",
					"qty": 5
				}
			]
		},
		{
			"item": "NoteBook",
			"instock": [
				{
					"warehouse": "B",
					"qty": 20,
					"price": "5$"
				}
			]
		}
	]
}

The Cloud can return JSON structures as column values. The Cloud enables you to use standard SQL functions to work with these JSON structures. The examples in this section use the following array:

[
     { "grade": "A", "score": 2 },
     { "grade": "A", "score": 6 },
     { "grade": "A", "score": 10 },
     { "grade": "A", "score": 9 },
     { "grade": "B", "score": 14 }
]

JSON_EXTRACT

SELECT Name, JSON_EXTRACT(grades,'[0].grade') AS Grade, JSON_EXTRACT(grades,'[0].score') AS Score FROM Students;

JSON_COUNT

SELECT Name, JSON_COUNT(grades,'[x]') AS NumberOfGrades FROM Students;

JSON_SUM

SELECT Name, JSON_SUM(score,'[x].score') AS TotalScore FROM Students;

JSON_MIN

SELECT Name, JSON_MIN(score,'[x].score') AS LowestScore FROM Students;

JSON_MAX

SELECT Name, JSON_MAX(score,'[x].score') AS HighestScore FROM Students;

DOCUMENT

The DOCUMENT function can be used to retrieve the entire document as a JSON string. See the following query and its result as an example:

SELECT DOCUMENT(*) FROM Customers;

{ "id": 12, "name": "Lohia Manufacturers Inc.", "address": { "street": "Main Street", "city": "Chapel Hill", "state": "NC"}, "offices": [ "Chapel Hill", "London", "New York" ], "annual_revenue": 35,600,000 }

The Cloud maps SQL queries into the corresponding MongoDB queries. A detailed description of all the transformations is out of scope, but we will describe some of the common elements that are used. The Cloud takes advantage of MongoDB features such as the aggregation framework to compute the desired results.

SELECT Queries

SELECT * FROM Users

db.users.find()

SELECT user_id, status 
FROM Users

db.users.find(
  {}, 
  { user_id: 1, status: 1, _id: 0 }
)

SELECT * 
FROM Users 
WHERE status = 'A'

db.users.find( 
  { status: "A" }
)

SELECT * 
FROM Users 
WHERE status = 'A' OR age=50

db.users.find(
  { $or: [ { status: "A" }, 
           { age: 50 } ] }
)

SELECT * 
FROM Users 
WHERE name LIKE 'A%'

db.users.find(
  {name: /^a/}
)

SELECT * FROM Users 
WHERE status = 'A'
ORDER BY user_id ASC

db.users.find( { status: "A" }.sort( { user_id: 1 } )

SELECT * 
FROM Users 
WHERE status = 'A' 
ORDER BY user_id DESC

db.users.find( {status: "A" }.sort( {user_id: -1} )

Aggregate Queries

SELECT Count(*) As Count 
FROM Orders

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: null, 
      count: { $sum: 1 } 
    } 
  } 
] )

SELECT Sum(price) As Total 
FROM Orders

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: null, 
      total: { $sum: "$price" } 
    }
  } 
] )

SELECT cust_id, Sum(price) As total 
FROM Orders 
GROUP BY cust_id 
ORDER BY total

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: "$cust_id", 
      total: { $sum: "$price" } 
    } 
  } ,
  { $sort: {total: 1 } }
] )

SELECT cust_id, ord_date, Sum(price) As total 
FROM Orders 
GROUP BY cust_id, ord_date 
HAVING total > 250

db.orders.aggregate( [ 
  { 
    $group: { 
      _id: { 
        cust_id: "$cust_id", 
        ord_date: { 
          month: { $month: "$ord_date" }, 
          day: { $dayOfMonth: "$ord_date" }, 
          year: { $year: "$ord_date"} 
        } 
      }, 
      total: { $sum: "$price" } 
    }
  }, 
  { $match: { total: { $gt: 250 } } } 
] )

Insert Statements

INSERT INTO users(user_id, age, status, [address.city], [address.postalcode]) 
VALUES ('bcd001', 45, 'A', 'Chapel Hill', 27517)

db.users.insert( 
  { user_id: "bcd001", age: 45, status: "A", address:{ city:"Chapel Hill", postalCode:27514} }
) 

INSERT INTO t1 ("c1") VALUES(('a1', 'a2', 'a3'))

db.users.insert({"c1": ['a1', 'a2', 'a3']})

INSERT INTO t1 ("c1") VALUES(())

db.users.insert({"c1": []})

INSERT INTO t1 ("a.b.c.c1") VALUES(('a1', 'a2', 'a3'))

db.users.insert("a":{"b":{"c":{"c1":['a1','a2', 'a3']}}})

Update Statements

UPDATE users 
SET status = 'C', [address.postalcode] = 90210
WHERE age > 25

db.users.update( 
  { age: { $gt: 25 } }, 
  { $set: { status: "C", address.postalCode: 90210 }, 
  { multi: true }
) 

Delete Statements

DELETE FROM users WHERE status = 'D'

db.users.remove( { status: "D" } )

You can extend the table schemas created with Automatic Schema Discovery by saving them into schema files. The schema files have a simple format that makes the schemas to edit.

Generating Schema Files

Set GenerateSchemaFiles to "OnStart" to persist schemas for all tables when you connect. You can also generate table schemas as needed: Set GenerateSchemaFiles to "OnUse" and execute a SELECT query to the table.

For example, consider a schema for the restaurants data set. This is a sample data set provided by MongoDB. To download the data set, follow the Getting Started with MongoDB guide.

Below is an example document from the collection:

{
  "address":{
    "building":"461",
      "coord":[
        -74.138492,
        40.631136
      ],
      "street":"Port Richmond Ave",
      "zipcode":"10302"
   },
   "borough":"Staten Island",
   "cuisine":"Other",
   "name":"Indian Oven",
   "restaurant_id":"50018994"
}

Importing the MongoDB Restaurant Data Set

You can use the mongoimport utility to import the data set:

mongoimport --db test --collection restaurants --drop --file dataset.json

Customizing a Schema

When GenerateSchemaFiles is set, the Cloud saves schemas into the folder specified by the Location property. You can then change column behavior in the resulting schema.

The following schema uses the other:bsonpath property to define where in the collection to retrieve the data for a particular column. Using this model you can flatten arbitrary levels of hierarchy.

The collection attribute specifies the collection to parse. The collection attribute gives you the flexibility to use multiple schemas for the same collection. If collection is not specified, the filename determines the collection that is parsed.

Below are the column definitions and the collection to extract the column values from. In Custom Schema Example, you will find the complete schema.

<rsb:script xmlns:rsb="http://www.rssbus.com/ns/rsbscript/2">  

  <rsb:info title="StaticRestaurants" description="Custom Schema for the MongoDB restaurants data set.">  
    <!-- Column definitions -->
    <attr  name="borough"   xs:type="string" other:bsonpath="$.borough"              />
    <attr  name="cuisine"   xs:type="string" other:bsonpath="$.cuisine"              />
    <attr  name="building"  xs:type="string" other:bsonpath="$.address.building"     />
    <attr  name="street"    xs:type="string" other:bsonpath="$.address.street"       />
    <attr  name="latitude"  xs:type="double" other:bsonpath="$.address.coord.0"      />
    <attr  name="longitude" xs:type="double" other:bsonpath="$.address.coord.1"      />
  </rsb:info>  

  <rsb:set attr="collection" value="restaurants"/>

</rsb:script>

This section contains an example of a complete schema that has been automatically generated by GenerateSchemaFiles. Set the Location property to the file directory that will contain the schema file. The schema consists of the following parts:

• The info section enables a relational view of a MongoDB object. For more details, see Custom Schema Definitions.

• The collection attribute specifies the name of the collection to be parsed. The collection attribute can be used to define multiple schemas for the same collection. If collection is not specified, the filename determines the collection that is parsed.

• The GET, POST, MERGE, and DELETE methods allow SELECT, INSERT, UPDATE, and DELETE commands to this table. The coreExecOperation operation is an internal implementation and can be copied as-is to your own custom schema file.

<rsb:script xmlns:rsb="http://www.rssbus.com/ns/rsbscript/2">  

  <rsb:info title="StaticRestaurants" description="Automatic GenerateSchemaFile">  
    <!-- Column definitions -->
    <attr  name="borough"           xs:type="string"  other:bsonpath="$.borough"          />
    <attr  name="cuisine"           xs:type="string"  other:bsonpath="$.cuisine"          />
    <attr  name="address_building"  xs:type="string"  other:bsonpath="$.address.building" />
    <attr  name="address_street"    xs:type="string"  other:bsonpath="$.address.street"   />
    <attr  name="address_coord_0"   xs:type="double"  other:bsonpath="$.address.coord.0"  />
    <attr  name="address_coord_1"   xs:type="double"  other:bsonpath="$.address.coord.1"  />
  </rsb:info>  

  <rsb:set attr="collection" value="restaurants"/>

  <rsb:script method="GET">
    <rsb:call op="coreExecOperation" out="toout">
      <rsb:push item="toout"/>
    </rsb:call>
  </rsb:script>

  <rsb:script method="POST">
    <rsb:call op="coreExecOperation" out="toout">
      <rsb:push item="toout"/>
    </rsb:call>
  </rsb:script>

  <rsb:script method="DELETE">
    <rsb:call op="coreExecOperation" out="toout">
      <rsb:push item="toout"/>
    </rsb:call>
  </rsb:script>

  <rsb:script method="MERGE">
    <rsb:call op="coreExecOperation" out="toout">
      <rsb:push item="toout"/>
    </rsb:call>
  </rsb:script>

</rsb:script>

Data Type Mappings

The Cloud maps types from the data source to the corresponding data type available in the schema. The table below documents these mappings.

You can query the system tables described in this section to access schema information, information on data source functionality, and batch operation statistics.

Schema Tables

The following tables return database metadata for MongoDB:

• sys_catalogs: Lists the available databases.
• sys_schemas: Lists the available schemas.
• sys_tables: Lists the available tables and views.
• sys_tablecolumns: Describes the columns of the available tables and views.
• sys_procedures: Describes the available stored procedures.
• sys_procedureparameters: Describes stored procedure parameters.
• sys_keycolumns: Describes the primary and foreign keys.
• sys_indexes: Describes the available indexes.

Data Source Tables

The following tables return information about how to connect to and query the data source:

• sys_connection_props: Returns information on the available connection properties.
• sys_sqlinfo: Describes the SELECT queries that the Cloud can offload to the data source.

Query Information Tables

The following table returns query statistics for data modification queries, including batch operations::

• sys_identity: Returns information about batch operations or single updates.

Lists the available databases.

The following query retrieves all databases determined by the connection string:

SELECT * FROM sys_catalogs

Columns

Lists the available schemas.

The following query retrieves all available schemas:

          SELECT * FROM sys_schemas
          

Columns

Lists the available tables.

The following query retrieves the available tables and views:

          SELECT * FROM sys_tables
          

Columns

Describes the columns of the available tables and views.

The following query returns the columns and data types for the [CData].[Sample].Customers table:

SELECT ColumnName, DataTypeName FROM sys_tablecolumns WHERE TableName='Customers' AND CatalogName='CData' AND SchemaName='Sample'

Columns

Lists the available stored procedures.

The following query retrieves the available stored procedures:

          SELECT * FROM sys_procedures
          

Columns

Describes stored procedure parameters.

The following query returns information about all of the input parameters for the EVAL stored procedure:

SELECT * FROM sys_procedureparameters WHERE ProcedureName='EVAL' AND Direction=1 OR Direction=2

Columns

Describes the primary and foreign keys.

The following query retrieves the primary key for the [CData].[Sample].Customers table:

         SELECT * FROM sys_keycolumns WHERE IsKey='True' AND TableName='Customers' AND CatalogName='CData' AND SchemaName='Sample'
          

Columns

Describes the foreign keys.

The following query retrieves all foreign keys which refer to other tables:

         SELECT * FROM sys_foreignkeys WHERE ForeignKeyType = 'FOREIGNKEY_TYPE_IMPORT'
          

Columns

Describes the primary keys.

The following query retrieves the primary keys from all tables and views:

         SELECT * FROM sys_primarykeys
          

Columns

Describes the available indexes. By filtering on indexes, you can write more selective queries with faster query response times.

The following query retrieves all indexes that are not primary keys:

          SELECT * FROM sys_indexes WHERE IsPrimary='false'
          

Columns

Returns information on the available connection properties and those set in the connection string.

When querying this table, the config connection string should be used:

jdbc:cdata:mongodb:config:

This connection string enables you to query this table without a valid connection.

The following query retrieves all connection properties that have been set in the connection string or set through a default value:

SELECT * FROM sys_connection_props WHERE Value <> ''

Columns

Describes the SELECT query processing that the Cloud can offload to the data source.

See SQL Compliance for SQL syntax details.

Discovering the Data Source's SELECT Capabilities

Below is an example data set of SQL capabilities. The following result set indicates the SELECT functionality that the Cloud can offload to the data source or process client side. Your data source may support additional SQL syntax. Some aspects of SELECT functionality are returned in a comma-separated list if supported; otherwise, the column contains NO.

The following query retrieves the operators that can be used in the WHERE clause:

SELECT * FROM sys_sqlinfo WHERE Name='SUPPORTED_OPERATORS'

Columns

Returns information about attempted modifications.

The following query retrieves the Ids of the modified rows in a batch operation:

         SELECT * FROM sys_identity
          

Columns

Stored procedures are function-like interfaces that extend the functionality of the Cloud beyond simple SELECT/INSERT/UPDATE/DELETE operations with MongoDB.

Stored procedures accept a list of parameters, perform their intended function, and then return, if applicable, any relevant response data from MongoDB, along with an indication of whether the procedure succeeded or failed.

CData Cloud - MongoDB Stored Procedures

Insert entire JSON documents to MongoDB as-is.

Input

Result Set Columns

Creates a schema file for the collection.

CreateSchema

Creates a local schema file (.rsd) from an existing table or view in the data model.

The schema file is created in the directory set in the Location connection property when this procedure is executed. You can edit the file to include or exclude columns, rename columns, or adjust column datatypes.

The Cloud checks the Location to determine if the names of any .rsd files match a table or view in the data model. If there is a duplicate, the schema file will take precedence over the default instance of this table in the data model. If a schema file is present in Location that does not match an existing table or view, a new table or view entry is added to the data model of the Cloud.

Input

Result Set Columns

Creates a schema file for the collection.

Input

Result Set Columns

Provides the ability to run JavaScript code on the MongoDB server.

Stored Procedure Specific Information

You can use the EVAL stored procedure to execute JavaScript functions as stored procedures:

EXEC EVAL @jsFunction = 'function() { return db.restaurants.findOne(); }'

EXEC EVAL @jsFunction = 'function() { db.system.js.save({ _id: "myAddFunction", value : function (x, y) { return x + y; } }); }'

EXEC EVAL @jsFunction = 'function() { return myAddFunction(1,1); }'

SELECT * FROM [system.js]

Input

Result Set Columns

Take a pass-through query to retrieve documents.

Input

Result Set Columns

Get the entire document as a string.

Input

Result Set Columns

This section details a selection of advanced features of the MongoDB Cloud.

User Defined Views

The Cloud allows you to define virtual tables, called user defined views, whose contents are decided by a pre-configured query. These views are useful when you cannot directly control queries being issued to the drivers. See User Defined Views for an overview of creating and configuring custom views.

SSL Configuration

Use SSL Configuration to adjust how Cloud handles TLS/SSL certificate negotiations. You can choose from various certificate formats; see the SSLServerCert property under "Connection String Options" for more information.

Firewall and Proxy

Configure the Cloud for compliance with Firewall and Proxy, including Windows proxies. You can also set up tunnel connections.

Query Processing

The Cloud offloads as much of the SELECT statement processing as possible to MongoDB and then processes the rest of the query in memory (client-side).

See Query Processing for more information.

Logging

See Logging for an overview of configuration settings that can be used to refine CData logging. For basic logging, you only need to set two connection properties, but there are numerous features that support more refined logging, where you can select subsets of information to be logged using the LogModules connection property.

The CData Cloud allows you to define a virtual table whose contents are decided by a pre-configured query. These are called User Defined Views, which are useful in situations where you cannot directly control the query being issued to the driver, e.g. when using the driver from a tool. The User Defined Views can be used to define predicates that are always applied. If you specify additional predicates in the query to the view, they are combined with the query already defined as part of the view.

There are two ways to create user defined views:

• Create a JSON-formatted configuration file defining the views you want.

Defining Views Using a Configuration File

You can also have multiple view definitions and control them using the UserDefinedViews connection property. When you use this property, only the specified views are seen by the Cloud.

This User Defined View configuration file is formatted as follows:

• Each root element defines the name of a view.
• Each root element contains a child element, called query, which contains the custom SQL query for the view.

For example:

{
	"MyView": {
		"query": "SELECT * FROM [CData].[Sample].Customers WHERE MyColumn = 'value'"
	},
	"MyView2": {
		"query": "SELECT * FROM MyTable WHERE Id IN (1,2,3)"
	}
}

"UserDefinedViews", "C:\\Users\\yourusername\\Desktop\\tmp\\UserDefinedViews.json"

Schema for User Defined Views

Working with User Defined Views

SELECT * FROM Customers WHERE City = 'Raleigh';

SELECT * FROM UserViews.RCustomers WHERE Status = 'Active';

SELECT * FROM Customers WHERE City = 'Raleigh' AND Status = 'Active';

Customizing the SSL Configuration

By default, the Cloud attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store.

To specify another certificate, see the SSLServerCert property for the available formats to do so.

Client SSL Certificates

The MongoDB Cloud also supports setting client certificates. Set the following to connect using a client certificate.

• SSLClientCert: The name of the certificate store for the client certificate.
• SSLClientCertType: The type of key store containing the TLS/SSL client certificate.
• SSLClientCertPassword: The password for the TLS/SSL client certificate.
• SSLClientCertSubject: The subject of the TLS/SSL client certificate.

Connecting Through a Firewall or Proxy

Set the following properties:

• To use a proxy-based firewall, set FirewallType, FirewallServer, and FirewallPort.
• To tunnel the connection, set FirewallType to TUNNEL.
• To authenticate, specify FirewallUser and FirewallPassword.
• To authenticate to a SOCKS proxy, additionally set FirewallType to SOCKS5.

Query Processing

For sources that do not support SQL-92, the Cloud offloads as much of SQL statement processing as possible to MongoDB and then processes the rest of the query in memory (client-side). This results in optimal performance.

For data sources with limited query capabilities, the Cloud handles transformations of the SQL query to make it simpler for the Cloud. The goal is to make smart decisions based on the query capabilities of the data source to push down as much of the computation as possible. The MongoDB Query Evaluation component examines SQL queries and returns information indicating what parts of the query the Cloud is not capable of executing natively.

The MongoDB Query Slicer component is used in more specific cases to separate a single query into multiple independent queries. The client-side Query Engine makes decisions about simplifying queries, breaking queries into multiple queries, and pushing down or computing aggregations on the client-side while minimizing the size of the result set.

There's a significant trade-off in evaluating queries, even partially, client-side. There are always queries that are impossible to execute efficiently in this model, and some can be particularly expensive to compute in this manner. CData always pushes down as much of the query as is feasible for the data source to generate the most efficient query possible and provide the most flexible query capabilities.

More Information

Capturing Cloud logging can be very helpful when diagnosing error messages or other unexpected behavior.

Basic Logging

You will simply need to set two connection properties to begin capturing Cloud logging.

• Logfile: A filepath which designates the name and location of the log file.
• Verbosity: This is a numerical value (1-5) that determines the amount of detail in the log. See the page in the Connection Properties section for an explanation of the five levels.
• MaxLogFileSize: When the limit is hit, a new log is created in the same folder with the date and time appended to the end. The default limit is 100 MB. Values lower than 100 kB will use 100 kB as the value instead.
• MaxLogFileCount: A string specifying the maximum file count of log files. When the limit is hit, a new log is created in the same folder with the date and time appended to the end and the oldest log file will be deleted. Minimum supported value is 2. A value of 0 or a negative value indicates no limit on the count.

Once this property is set, the Cloud will populate the log file as it carries out various tasks, such as when authentication is performed or queries are executed. If the specified file doesn't already exist, it will be created.

Log Verbosity

The verbosity level determines the amount of detail that the Cloud reports to the Logfile. Verbosity levels from 1 to 5 are supported. These are described in the following list:

The Verbosity should not be set to greater than 1 for normal operation. Substantial amounts of data can be logged at higher verbosities, which can delay execution times.

To refine the logged content further by showing/hiding specific categories of information, see LogModules.

Sensitive Data

• Verbosity 3: The full body of the request and the response, which includes all the data returned by the Cloud
• Verbosity 4: SSL certificates
• Verbosity 5: Any extra transfer data not included at Verbosity 3, such as non human-readable binary transfer data

Best Practices for Data Security

Although we mask sensitive values, such as passwords, in the connection string and any request in the log, it is always best practice to review the logs for any sensitive information before sharing outside your organization.

Advanced Logging

You may want to refine the exact information that is recorded to the log file. This can be accomplished using the LogModules property.

This property allows you to filter the logging using a semicolon-separated list of logging modules.

All modules are four characters long. Please note that modules containing three letters have a required trailing blank space. The available modules are:

• EXEC: Query Execution. Includes execution messages for original SQL queries, parsed SQL queries, and normalized SQL queries. Query and page success/failure messages appear here as well.
• INFO: General Information. Includes the connection string, driver version (build number), and initial connection messages.
• HTTP: HTTP Protocol messages. Includes HTTP requests/responses (including POST messages), as well as Kerberos related messages.
• SSL : SSL certificate messages.
• OAUT: OAuth related failure/success messages.
• SQL : Includes SQL transactions, SQL bulk transfer messages, and SQL result set messages.
• META: Metadata cache and schema messages.
• TCP : Incoming and Ongoing raw bytes on TCP transport layer messages.

LogModules=INFO;EXEC;SSL ;SQL ;META;

Note that these modules refine the information as it is pulled after taking the Verbosity into account.

The CData Cloud supports several operations on data, including querying, deleting, modifying, and inserting.

SELECT Statements

See SELECT Statements for a syntax reference and examples.

See NoSQL Database for information on the capabilities of the MongoDB API.

INSERT Statements

See INSERT Statements for a syntax reference and examples.

UPDATE Statements

The primary key _id is required to update a record. See UPDATE Statements for a syntax reference and examples.

DELETE Statements

The primary key _id is required to delete a record. See DELETE Statements for a syntax reference and examples.

CREATE TABLE Statements

See CREATE TABLE Statements for a syntax reference and examples.

DROP TABLE Statements

See DROP TABLE Statements for a syntax reference and examples.

EXECUTE Statements

Use EXECUTE or EXEC statements to execute stored procedures. See EXECUTE Statements for a syntax reference and examples.

Names and Quoting

• Table and column names are considered identifier names; as such, they are restricted to the following characters: [A-Z, a-z, 0-9, _:@].
• To use a table or column name with characters not listed above, the name must be quoted using square brackets ([name]) in any SQL statement.
• Parameter names can optionally start with the @ symbol (e.g., @p1 or @CustomerName) and cannot be quoted.
• Strings must be quoted using single quotes (e.g., 'John Doe').

A SELECT statement can consist of the following basic clauses.

• SELECT
• INTO
• FROM
• JOIN
• WHERE
• GROUP BY
• HAVING
• UNION
• ORDER BY
• LIMIT

SELECT Syntax

The following syntax diagram outlines the syntax supported by the SQL engine of the Cloud:

SELECT {
  [ TOP <numeric_literal> | DISTINCT ]
  { 
    * 
    | { 
        <expression> [ [ AS ] <column_reference> ] 
        | { <table_name> | <correlation_name> } .* 
      } [ , ... ] 
  }
  [ INTO csv:// [ filename= ] <file_path> [ ;delimiter=tab ] ]
  { 
    FROM <table_reference> [ [ AS ] <identifier> ] 
  } [ , ... ]
  [ [  
      INNER | { { LEFT | RIGHT | FULL } [ OUTER ] } 
    ] JOIN <table_reference> [ ON <search_condition> ] [ [ AS ] <identifier> ] 
  ] [ ... ] 
  [ WHERE <search_condition> ]
  [ GROUP BY <column_reference> [ , ... ]
  [ HAVING <search_condition> ]
  [ UNION [ ALL ] <select_statement> ]
  [ 
    ORDER BY 
    <column_reference> [ ASC | DESC ] [ NULLS FIRST | NULLS LAST ]
  ]
  [ 
    LIMIT <expression>
    [ 
      { OFFSET | , }
      <expression> 
    ]
  ] 
} | SCOPE_IDENTITY() 

<expression> ::=
  | <column_reference>
  | @ <parameter> 
  | ?
  | COUNT( * | { [ DISTINCT ] <expression> } )
  | { AVG | MAX | MIN | SUM | COUNT } ( <expression> ) 
  | NULLIF ( <expression> , <expression> ) 
  | COALESCE ( <expression> , ... ) 
  | CASE <expression>
      WHEN { <expression> | <search_condition> } THEN { <expression> | NULL } [ ... ]
    [ ELSE { <expression> | NULL } ]
    END 
  | <literal>
  | <sql_function> 

<search_condition> ::= 
  {
    <expression> { = | > | < | >= | <= | <> | != | LIKE | NOT LIKE | IN | NOT IN | IS NULL | IS NOT NULL | AND | OR | CONTAINS | BETWEEN } [ <expression> ]
  } [ { AND | OR } ... ] 

Examples

1. Return all columns:
   

   SELECT * FROM [CData].[Sample].Customers

2. Rename a column:
   

   SELECT [CompanyName] AS MY_CompanyName FROM [CData].[Sample].Customers

3. Cast a column's data as a different data type:
   

   SELECT CAST(Balance AS VARCHAR) AS Str_Balance FROM [CData].[Sample].Customers

4. Search data:
   

   SELECT * FROM [CData].[Sample].Customers WHERE Country = 'US'

5. Return the number of items matching the query criteria:
   

   SELECT COUNT(*) AS MyCount FROM [CData].[Sample].Customers 

6. Return the number of unique items matching the query criteria:
   

   SELECT COUNT(DISTINCT CompanyName) FROM [CData].[Sample].Customers 

7. Return the unique items matching the query criteria:
   

   SELECT DISTINCT CompanyName FROM [CData].[Sample].Customers 

8. Summarize data:
   

   SELECT CompanyName, MAX(Balance) FROM [CData].[Sample].Customers GROUP BY CompanyName

   See Aggregate Functions for details.
9. Retrieve data from multiple tables.
   

   SELECT restaurants.name, zips.city FROM restaurants INNER JOIN zips ON restaurants.address_zipcode = zips.C_id

   See JOIN Queries for details.
10. Sort a result set in ascending order:
    

    SELECT City, CompanyName FROM [CData].[Sample].Customers  ORDER BY CompanyName ASC

11. Restrict a result set to the specified number of rows:
    

    SELECT City, CompanyName FROM [CData].[Sample].Customers LIMIT 10 

12. Parameterize a query to pass in inputs at execution time. This enables you to create prepared statements and mitigate SQL injection attacks.
    

    SELECT * FROM [CData].[Sample].Customers WHERE Country = @param

Pseudo Columns

Some input-only fields are available in SELECT statements. These fields, called pseudo columns, do not appear as regular columns in the results, yet may be specified as part of the WHERE clause. You can use pseudo columns to access additional features from MongoDB.

    SELECT * FROM [CData].[Sample].Customers WHERE MyPseudocolumn = 'MyValue'
    

COUNT

Returns the number of rows matching the query criteria.

SELECT COUNT(*) FROM [CData].[Sample].Customers WHERE Country = 'US'

COUNT(DISTINCT)

Returns the number of distinct, non-null field values matching the query criteria.

SELECT COUNT(DISTINCT City) AS DistinctValues FROM [CData].[Sample].Customers WHERE Country = 'US'

AVG

Returns the average of the column values.

SELECT CompanyName, AVG(Balance) FROM [CData].[Sample].Customers WHERE Country = 'US'  GROUP BY CompanyName

MIN

Returns the minimum column value.

SELECT MIN(Balance), CompanyName FROM [CData].[Sample].Customers WHERE Country = 'US' GROUP BY CompanyName

MAX

Returns the maximum column value.

SELECT CompanyName, MAX(Balance) FROM [CData].[Sample].Customers WHERE Country = 'US' GROUP BY CompanyName

SUM

Returns the total sum of the column values.

SELECT SUM(Balance) FROM [CData].[Sample].Customers WHERE Country = 'US'

The CData Cloud supports joins of a nested array with its parent document and joins of multiple collections.

Joining Nested Structures

The Cloud expects the left part of the join is the array document you want to flatten vertically. Disable SupportEnhancedSQL to join nested MongoDB documents. This type of query is supported through the MongoDB API.

For example, consider the following query from MongoDB's restaurants collection:

SELECT [restaurants].[restaurant_id], [restaurants].name, [restaurants.grades].* 
FROM [restaurants.grades]
JOIN [restaurants] 
WHERE [restaurants].name = 'Morris Park Bake Shop'

Joining Multiple Collections

You can join multiple collections just like you would join tables in a relational database. Set SupportEnhancedSQL to True to execute these types of joins. The following examples use the restaurants and zips collections available in the MongoDB documentation.

The query below returns the restaurant records that exist, if any, for each ZIP code:

SELECT z.city, r.name, r.borough, r.cuisine, r.[address.zipcode]
FROM zips z
LEFT JOIN restaurants r
ON r.[address.zipcode] = z._id

The query below returns records from both tables that match the join condition:

SELECT z.city, r.name, r.borough, r.cuisine, r.[address.zipcode]
FROM restaurants r
INNER JOIN zips z
ON r.[address.zipcode] = z._id

The following date literal functions can be used to filter date fields using relative intervals. Note that while the <, >, and = operators are supported for these functions, <= and >= are not.

L_TODAY()

The current day.

  SELECT * FROM MyTable WHERE MyDateField = L_TODAY()

L_YESTERDAY()

The previous day.

  SELECT * FROM MyTable WHERE MyDateField = L_YESTERDAY()

L_TOMORROW()

The following day.

  SELECT * FROM MyTable WHERE MyDateField = L_TOMORROW()

L_LAST_WEEK()

Every day in the preceding week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_WEEK()

L_THIS_WEEK()

Every day in the current week.

  SELECT * FROM MyTable WHERE MyDateField = L_THIS_WEEK()

L_NEXT_WEEK()

Every day in the following week.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_WEEK()

• L_LAST/L_THIS/L_NEXT MONTH
• L_LAST/L_THIS/L_NEXT QUARTER
• L_LAST/L_THIS/L_NEXT YEAR

L_LAST_N_DAYS(n)

The previous n days, excluding the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_DAYS(3)

L_NEXT_N_DAYS(n)

The following n days, including the current day.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_DAYS(3)

• L_LAST/L_NEXT_90_DAYS

L_LAST_N_WEEKS(n)

Every day in every week, starting n weeks before current week, and ending in the previous week.

  SELECT * FROM MyTable WHERE MyDateField = L_LAST_N_WEEKS(3)

L_NEXT_N_WEEKS(n)

Every day in every week, starting the following week, and ending n weeks in the future.

  SELECT * FROM MyTable WHERE MyDateField = L_NEXT_N_WEEKS(3)

• L_LAST/L_NEXT_N_MONTHS(n)
• L_LAST/L_NEXT_N_QUARTERS(n)
• L_LAST/L_NEXT_N_YEARS(n)

You can use the SELECT INTO statement to export formatted data to a file.

Data Export with an SQL Query

The following query exports data into a file formatted in comma-separated values (CSV):

SELECT City, CompanyName INTO [csv://[CData].[Sample].Customers.txt] FROM [[CData].[Sample].Customers] WHERE Country = 'US'

SELECT City, CompanyName INTO [csv://[CData].[Sample].Customers.txt;delimiter=tab] FROM [[CData].[Sample].Customers] WHERE Country = 'US'

The Cloud provides functions that are similar to those that are available with most standard databases. These functions are implemented in the CData provider engine and thus are available across all data sources with the same consistent API. Three categories of functions are available: string, date, and math.

The Cloud interprets all SQL function inputs as either strings or column identifiers, so you need to escape all literals as strings, with single quotes. For example, contrast the SQL Server syntax and Cloud syntax for the DATENAME function:

• SQL Server:
  

  SELECT DATENAME(yy,GETDATE())

• Cloud:
  

  SELECT DATENAME('yy',GETDATE())

String Functions

These functions perform string manipulations and return a string value. See STRING Functions for more details.

SELECT CONCAT(firstname, space(4), lastname) FROM [CData].[Sample].Customers WHERE Country = 'US'

Date Functions

These functions perform date and date time manipulations. See DATE Functions for more details.

SELECT CURRENT_TIMESTAMP() FROM [CData].[Sample].Customers

Math Functions

These functions provide mathematical operations. See MATH Functions for more details.

SELECT RAND() FROM [CData].[Sample].Customers

Function Parameters and Nesting SQL Functions

SELECT CONCAT('Mr.', SPACE(2), firstname, SPACE(4), lastname) FROM [CData].[Sample].Customers

ASCII(character_expression)

Returns the ASCII code value of the left-most character of the character expression.

• character_expression: The character expression.

                      SELECT ASCII('0');
                      --  Result: 48
                    

CHAR(integer_expression)

Converts the integer ASCII code to the corresponding character.

• integer_expression: The integer from 0 through 255.

                      SELECT CHAR(48);
                      -- Result: '0'
                    

CHARINDEX(expressionToFind ,expressionToSearch [,start_location ])

Returns the starting position of the specified expression in the character string.

• expressionToFind: The character expression to find.
• expressionToSearch: The character expression, typically a column, to search.
• start_location: The optional character position to start searching for expressionToFind in expressionToSearch.

                      SELECT CHARINDEX('456', '0123456');
                      -- Result: 4

                      SELECT CHARINDEX('456', '0123456', 5);
                      -- Result: -1
                    

CHAR_LENGTH(character_expression),

Returns the number of UTF-8 characters present in the expression.

• character_expression: The set of characters to be be evaluated for length.

				 SELECT CHAR_LENGTH('sample text') FROM Account LIMIT 1
				 -- Result: 11			
				

CONCAT(string_value1, string_value2 [, string_valueN])

Returns the string that is the concatenation of two or more string values.

• string_value1: The first string to be concatenated.
• string_value2: The second string to be concatenated.
• *: The optional additional strings to be concatenated.

                      SELECT CONCAT('Hello, ', 'world!');
                      -- Result: 'Hello, world!'
                    

CONTAINS(expressionToSearch, expressionToFind)

Returns 1 if expressionToFind is found within expressionToSearch; otherwise, 0.

• expressionToSearch: The character expression, typically a column, to search.
• expressionToFind: The character expression to find.

                      SELECT CONTAINS('0123456', '456');
                      -- Result: 1

                      SELECT CONTAINS('0123456', 'Not a number');
                      -- Result: 0
                    

ENDSWITH(character_expression, character_suffix)

Returns 1 if character_expression ends with character_suffix; otherwise, 0.

• character_expression: The character expression.
• character_suffix: The character suffix to search for.

                      SELECT ENDSWITH('0123456', '456');
                      -- Result: 1

                      SELECT ENDSWITH('0123456', '012');
                      -- Result: 0
                    

FILESIZE(uri)

Returns the number of bytes present in the file at the specified file path.

• uri: The path of the file to read the size from.

				SELECT FILESIZE('C:/Users/User1/Desktop/myfile.txt');
				-- Result: 23684
				

FORMAT(value [, parseFormat], format )

Returns the value formatted with the specified format.

• value: The string to format.
• format: The string specifying the output syntax of the date or numeric format.
• parseFormat: The string specifying the input syntax of the date value. Not applicable to numeric types.

                      SELECT FORMAT(12.34, '#');
                      -- Result: 12

                      SELECT FORMAT(12.34, '#.###');
                      -- Result: 12.34

                      SELECT FORMAT(1234, '0.000E0');
                      -- Result: 1.234E3
                      
                      SELECT FORMAT('2019/01/01', 'yyyy-MM-dd');
                      -- Result: 2019-01-01
                      
                      SELECT FORMAT('20190101', 'yyyyMMdd', 'yyyy-MM-dd');
                      -- Result: '2019-01-01'
                    

FROM_UNIXTIME(time, issecond)

Returns a representation of the unix_timestamp argument as a value in YYYY-MM-DD HH:MM:SS expressed in the current time zone.

• time: The time stamp value from epoch time. Milliseconds are accepted.
• issecond: Indicates the time stamp value is milliseconds to epoch time.

                      SELECT FROM_UNIXTIME(1540495231, 1);
                      -- Result: 2018-10-25 19:20:31

                      SELECT FROM_UNIXTIME(1540495357385, 0);
                      -- Result: 2018-10-25 19:22:37
                    

HASHBYTES(algorithm, value)

Returns the hash of the input value as a byte array using the given algorithm. The supported algorithms are MD5, SHA1, SHA2_256, SHA2_512, SHA3_224, SHA3_256, SHA3_384, and SHA3_512.

• algorithm: The algorithm to use for hashing. Must be one of MD5, SHA1, SHA2_256, SHA2_512, SHA3_224, SHA3_256, SHA3_384, or SHA3_512.
• value: The value to hash. Must be either a string or byte array.

                      SELECT HASHBYTES('MD5', 'Test');
                      -- Result (byte array): 0x0CBC6611F5540BD0809A388DC95A615B
                    

INDEXOF(expressionToSearch, expressionToFind [,start_location ])

Returns the starting position of the specified expression in the character string.

• expressionToSearch: The character expression, typically a column, to search.
• expressionToFind: The character expression to find.
• start_location: The optional character position to start searching for expressionToFind in expressionToSearch.

                      SELECT INDEXOF('0123456', '456');
                      -- Result: 4

                      SELECT INDEXOF('0123456', '456', 5);
                      -- Result: -1
                    

ISNULL ( check_expression , replacement_value )

Replaces null with the specified replacement value.

• check_expression: The expression to be checked for null.
• replacement_value: The expression to be returned if check_expression is null.

                      SELECT ISNULL(42, 'Was NULL');
                      -- Result: 42

                      SELECT ISNULL(NULL, 'Was NULL');
                      -- Result: 'Was NULL'
                    

JSON_AVG(json, jsonpath)

Computes the average value of a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_AVG('[1,2,3,4,5]', '$[x]');
                      -- Result: 3

                      SELECT JSON_AVG('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 3

                      SELECT JSON_AVG('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 4.5
                    

JSON_COUNT(json, jsonpath)

Returns the number of elements in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_COUNT('[1,2,3,4,5]', '$[x]');
                      -- Result: 5

                      SELECT JSON_COUNT('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 5

                      SELECT JSON_COUNT('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 2
                    

JSON_EXTRACT(json, jsonpath)

Selects any value in a JSON array or object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to extract.
• jsonpath: The XPath used to select the nodes. The JSONPath must be a string constant. The values of the nodes selected will be returned in a token-separated list.

                      SELECT JSON_EXTRACT('{"test": {"data": 1}}', '$.test');
                      -- Result: '{"data":1}'

                      SELECT JSON_EXTRACT('{"test": {"data": 1}}', '$.test.data');
                      -- Result: 1

                      SELECT JSON_EXTRACT('{"test": {"data": [1, 2, 3]}}', '$.test.data[1]');
                      -- Result: 2
                    

JSON_MAX(json, jsonpath)

Gets the maximum value in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_MAX('[1,2,3,4,5]', '$[x]');
                      -- Result: 5

                      SELECT JSON_MAX('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 5

                      SELECT JSON_MAX('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[..3]');
                      -- Result: 4
                    

JSON_MIN(json, jsonpath)

Gets the minimum value in a JSON array within a JSON object. The path to the array is specified in the jsonpath argument. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_MIN('[1,2,3,4,5]', '$[x]');
                      -- Result: 1

                      SELECT JSON_MIN('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 1

                      SELECT JSON_MIN('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 4
                    

JSON_SUM(json, jsonpath)

Computes the summary value in JSON according to the JSONPath expression. Return value is numeric or null.

• json: The JSON document to compute.
• jsonpath: The JSONPath used to select the nodes. [x], [2..], [..8], or [1..12] are accepted. [x] selects all nodes.

                      SELECT JSON_SUM('[1,2,3,4,5]', '$[x]');
                      -- Result: 15

                      SELECT JSON_SUM('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[x]');
                      -- Result: 15

                      SELECT JSON_SUM('{"test": {"data": [1,2,3,4,5]}}', '$.test.data[3..]');
                      -- Result: 9
                    

LEFT ( character_expression , integer_expression )

Returns the specified number of characters counting from the left of the specified string.

• character_expression: The character expression.
• integer_expression: The positive integer that specifies how many characters will be returned counting from the left of character_expression.

                      SELECT LEFT('1234567890', 3);
                      -- Result: '123'
                    

LEN(string_expression)

Returns the number of characters of the specified string expression.

• string_expression: The string expression.

                      SELECT LEN('12345');
                      -- Result: 5
                    

LOCATE(substring,string)

Returns an integer representing how many characters into the string the substring appears.

• substring: The substring to find inside larger string.
• string: The larger string that will be searched for the substring.

				SELECT LOCATE('sample','XXXXXsampleXXXXX');
				-- Result: 6
				

LOWER ( character_expression )

Returns the character expression with the uppercase character data converted to lowercase.

• character_expression: The character expression.

                      SELECT LOWER('MIXED case');
                      -- Result: 'mixed case'
                    

LTRIM(character_expression)

Returns the character expression with leading blanks removed.

• character_expression: The character expression.

                      SELECT LTRIM('     trimmed');
                      -- Result: 'trimmed'
                    

MASK(string_expression, mask_character [, start_index [, end_index ]])

Replaces the characters between start_index and end_index with the mask_character within the string.

• string_expression: The string expression to be searched.
• mask_character: The character to mask with.
• start_index: The optional number of characters to leave unmasked at beginning of string. Defaults to 0.
• end_index: The optional number of characters to leave unmasked at end of string. Defaults to 0.

                        SELECT MASK('1234567890','*',);
                        -- Result: '**********'
                        SELECT MASK('1234567890','*', 4);
                        -- Result: '1234******'
                        SELECT MASK('1234567890','*', 4, 2);
                        -- Result: '1234****90'  
                    

NCHAR(integer_expression)

Returns the Unicode character with the specified integer code as defined by the Unicode standard.

• integer_expression: The integer from 0 through 255.

OCTET_LENGTH(character_expression),

Returns the number of bytes present in the expression.

• character_expression: The set of characters to be be evaluated.

				 SELECT OCTET_LENGTH('text') FROM Account LIMIT 1
				 -- Result: 4
				

PATINDEX(pattern, expression)

Returns the starting position of the first occurrence of the pattern in the expression. Returns 0 if the pattern is not found.

• pattern: The character expression that contains the sequence to be found. The wild-card character % can be used only at the start or end of the expression.
• expression: The expression, typically a column, to search for the pattern.

                      SELECT PATINDEX('123%', '1234567890');
                      -- Result: 1

                      SELECT PATINDEX('%890', '1234567890');
                      -- Result: 8

                      SELECT PATINDEX('%456%', '1234567890');
                      -- Result: 4
                    

POSITION(expressionToFind IN expressionToSearch)

Returns the starting position of the specified expression in the character string.

• expressionToFind: The character expression to find.
• expressionToSearch: The character expression, typically a column, to search.

                      SELECT POSITION('456' IN '123456');
                      -- Result: 4

                      SELECT POSITION('x' IN '123456');
                      -- Result: 0
                    

QUOTENAME(character_string [, quote_character])

Returns a valid SQL Server-delimited identifier by adding the necessary delimiters to the specified Unicode string.

• character_string: The string of Unicode character data. The string is limited to 128 characters. Inputs greater than 128 characters return null.
• quote_character: The optional single character to be used as the delimiter. Can be a single quotation mark, a left or right bracket, or a double quotation mark. If quote_character is not specified brackets are used.

                      SELECT QUOTENAME('table_name');
                      -- Result: '[table_name]'

                      SELECT QUOTENAME('table_name', '"');
                      -- Result: '"table_name"'

                      SELECT QUOTENAME('table_name', '[');
                      -- Result: '[table_name]'
                    

REPLACE(string_expression, string_pattern, string_replacement)

Replaces all occurrences of a string with another string.

• string_expression: The string expression to be searched. Can be a character or binary data type.
• string_pattern: The substring to be found. Cannot be an empty string.
• string_replacement: The replacement string.

                      SELECT REPLACE('1234567890', '456', '|');
                      -- Result: '123|7890'

                      SELECT REPLACE('123123123', '123', '.');
                      -- Result: '...'

                      SELECT REPLACE('1234567890', 'a', 'b');
                      -- Result: '1234567890'
                    

REPLICATE ( string_expression ,integer_expression )

Repeats the string value the specified number of times.

• string_expression: The string to replicate.
• integer_expression: The repeat count.

                      SELECT REPLACE('x', 5);
                      -- Result: 'xxxxx'
                    

REVERSE ( string_expression )

Returns the reverse order of the string expression.

• string_expression: The string.

                      SELECT REVERSE('1234567890');
                      -- Result: '0987654321'
                    

RIGHT ( character_expression , integer_expression )

Returns the right part of the string with the specified number of characters.

• character_expression: The character expression.
• integer_expression: The positive integer that specifies how many characters of the character expression will be returned.

                      SELECT RIGHT('1234567890', 3);
                      -- Result: '890'
                    

RTRIM(character_expression)

Returns the character expression after it removes trailing blanks.

• character_expression: The character expression.

                      SELECT RTRIM('trimmed     ');
                      -- Result: 'trimmed'
                    

SOUNDEX(character_expression)

Returns the four-character Soundex code, based on how the string sounds when spoken.

• character_expression: The alphanumeric expression of character data.

                      SELECT SOUNDEX('smith');
                      -- Result: 'S530'
                    

SPACE(repeatcount)

Returns the string that consists of repeated spaces.

• repeatcount: The number of spaces.

                      SELECT SPACE(5);
                      -- Result: '     '
                    

SPLIT(string, delimiter, offset)

Returns a section of the string between to delimiters.

• string: The string to split.
• delimiter: The character to split the string with.
• offset: The number of the split to return. Positive numbers are treated as offsets from the left, and negative numbers are treated as offsets from the right.

                      SELECT SPLIT('a/b/c/d', '/', 1);
                      -- Result: 'a'
                      SELECT SPLIT('a/b/c/d', '/', -2);
                      -- Result: 'c'
                    

STARTSWITH(character_expression, character_prefix)

Returns 1 if character_expression starts with character_prefix; otherwise, 0.

• character_expression: The character expression.
• character_prefix: The character prefix to search for.

                      SELECT STARTSWITH('0123456', '012');
                      -- Result: 1

                      SELECT STARTSWITH('0123456', '456');
                      -- Result: 0
                    

STR ( float_expression [ , integer_length [ , integer_decimal ] ] )

Returns the character data converted from the numeric data. For example, STR(123.45, 6, 1) returns 123.5.

• float_expression: The float expression.
• length: The optional total length to return. This includes decimal point, sign, digits, and spaces. The default is 10.
• decimal: The optional number of places to the right of the decimal point. The decimal must be less than or equal to 16.

                      SELECT STR('123.456');
                      -- Result: '123'

                      SELECT STR('123.456', 2);
                      -- Result: '**'

                      SELECT STR('123.456', 10, 2);
                      -- Result: '123.46'
                    

STUFF(character_expression , integer_start , integer_length , replaceWith_expression)

Inserts a string into another string. It deletes the specified length of characters in the first string at the start position and then inserts the second string into the first string at the start position.

• character_expression: The string expression.
• start: The integer value that specifies the location to start deletion and insertion. If start or length is negative, null is returned. If start is longer than the string to be modified, character_expression, null is returned.
• length: The integer that specifies the number of characters to delete. If length is longer than character_expression, deletion occurs up to the last character in replaceWith_expression.
• replaceWith_expression: The expression of character data that will replace length characters of character_expression beginning at the start value.

                      SELECT STUFF('1234567890', 3, 2, 'xx');
                      -- Result: '12xx567890'
                    

SUBSTRING(string_value FROM start FOR length)

Returns the part of the string with the specified length; starts at the specified index.

• string_value: The character string.
• start: The positive integer that specifies the start index of characters to return.
• length: Optional. The positive integer that specifies how many characters will be returned.

                      SELECT SUBSTRING('1234567890' FROM 3 FOR 2);
                      -- Result: '34'

                      SELECT SUBSTRING('1234567890' FROM 3);
                      -- Result: '34567890'
                    

TOSTRING(string_value1)

Converts the value of this instance to its equivalent string representation.

• string_value1: The string to be converted.

                      SELECT TOSTRING(123);
                      -- Result: '123'

                      SELECT TOSTRING(123.456);
                      -- Result: '123.456'

                      SELECT TOSTRING(null);
                      -- Result: ''
                    

TRIM(trimspec trimchar FROM string_value)

Returns the character expression with leading and/or trailing blanks removed.

• trimspec: Optional. If included must be one of the keywords BOTH, LEADING or TRAILING.
• trimchar: Optional. If included should be a one-character string value.
• string_value: The string value to trim.

                      SELECT TRIM('     trimmed     ');
                      -- Result: 'trimmed'

                      SELECT TRIM(LEADING FROM '     trimmed     ');
                      -- Result: 'trimmed     '

                      SELECT TRIM('-' FROM '-----trimmed-----');
                      -- Result: 'trimmed'

                      SELECT TRIM(BOTH '-' FROM '-----trimmed-----');
                      -- Result: 'trimmed'

                      SELECT TRIM(TRAILING '-' FROM '-----trimmed-----');
                      -- Result: '-----trimmed'
                    

UNICODE(ncharacter_expression)

Returns the integer value defined by the Unicode standard of the first character of the input expression.

• ncharacter_expression: The Unicode character expression.

UPPER ( character_expression )

Returns the character expression with lowercase character data converted to uppercase.

• character_expression: The character expression.

                      SELECT UPPER('MIXED case');
                      -- Result: 'MIXED CASE'
                    

XML_EXTRACT(xml, xpath [, separator])

Extracts an XML document using the specified XPath to flatten the XML. A comma is used to separate the outputs by default, but this can be changed by specifying the third parameter.

• xml: The XML document to extract.
• xpath: The XPath used to select the nodes. The nodes selected will be returned in a token-separated list.
• separator: The optional token used to separate the items in the flattened response. If this is not specified, the separator will be a comma.

                      SELECT XML_EXTRACT('<vowels><ch>a</ch><ch>e</ch><ch>i</ch><ch>o</ch><ch>u</ch></vowels>', '/vowels/ch');
                      -- Result: 'a,e,i,o,u'

                      SELECT XML_EXTRACT('<vowels><ch>a</ch><ch>e</ch><ch>i</ch><ch>o</ch><ch>u</ch></vowels>', '/vowels/ch', ';');
                      -- Result: 'a;e;i;o;u'
                    

CURRENT_DATE()

Returns the current date value.

                  SELECT CURRENT_DATE();
                  -- Result: 2018-02-01
                

CURRENT_TIMESTAMP()

Returns the current time stamp of the database system as a datetime value. This value is equal to GETDATE and SYSDATETIME, and is always in the local timezone.

                  SELECT CURRENT_TIMESTAMP();
                  -- Result: 2018-02-01 03:04:05
                

DATEADD (datepart , integer_number , date [, dateformat])

Returns the datetime value that results from adding the specified number (a signed integer) to the specified date part of the date.

• datepart: The part of the date to add the specified number to. The valid values and abbreviations are year (yy, yyyy), quarter (qq, q), month (mm, m), dayofyear (dy, y), day (dd, d), week (wk, ww), weekday (dw), hour (hh), minute (mi, n), second (ss, s), and millisecond (ms).
• number: The number to be added.
• date: The expression of the datetime data type.
• dateformat: The optional output date format.

                  SELECT DATEADD('d', 5, '2018-02-01');
                  -- Result: 2018-02-06

                  SELECT DATEADD('hh', 5, '2018-02-01 00:00:00');
                  -- Result: 2018-02-01 05:00:00
                

DATEDIFF ( datepart , startdate , enddate )

Returns the difference (a signed integer) of the specified time interval between the specified start date and end date.

• datepart: The part of the date that is the time interval of the difference between the start date and end date. The valid values and abbreviations are day (dd, d), hour (hh), minute (mi, n), second (ss, s), and millisecond (ms).
• startdate: The datetime expression of the start date.
• enddate: The datetime expression of the end date.

                  SELECT DATEDIFF('d', '2018-02-01', '2018-02-10');
                  -- Result: 9

                  SELECT DATEDIFF('hh', '2018-02-01 00:00:00', '2018-02-01 12:00:00');
                  -- Result: 12
                

DATEFROMPARTS(integer_year, integer_month, integer_day)

Returns the datetime value for the specified year, month, and day.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.

                    SELECT DATEFROMPARTS(2018, 2, 1);
                    -- Result: 2018-02-01
                  

DATENAME(datepart , date)

Returns the character string that represents the specified date part of the specified date.

• datepart: The part of the date to return. The valid values and abbreviations are year (yy, yyyy), quarter (qq, q), month (mm, m), dayofyear (dy, y), day (dd, d), week (wk, ww), weekday (dw), hour (hh), minute (mi, n), second (ss, s), millisecond (ms), microsecond (mcs), nanosecond (ns), and TZoffset (tz).
• date: The datetime expression.

                     SELECT DATENAME('yy', '2018-02-01');
                     -- Result: '2018'

                     SELECT DATENAME('dw', '2018-02-01');
                     -- Result: 'Thursday'
                   

DATEPART(datepart, date [,integer_datefirst])

Returns a character string that represents the specified date part of the specified date.

• datepart: The part of the date to return. The valid values and abbreviations are year (yy, yyyy), quarter (qq, q), month (mm, m), dayofyear (dy, y), day (dd, d), week (wk, ww), weekday (dw), hour (hh), minute (mi, n), second (ss, s), millisecond (ms), microsecond (mcs), nanosecond (ns), TZoffset (tz), ISODOW, ISO_WEEK (isoweek, isowk,isoww), and ISOYEAR.
• date: The datetime string.
• datefirst: The optional integer representing the first day of the week. The default is 7, Sunday.

                    SELECT DATEPART('yy', '2018-02-01');
                    -- Result: 2018

                    SELECT DATEPART('dw', '2018-02-01');
                    -- Result: 5
                  

DATETIME2FROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute, integer_seconds, integer_fractions, integer_precision)

Returns the datetime value for the specified date parts.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• fractions: The integer expression specifying the fractions of the second.
• precision: The integer expression specifying the precision of the fraction.

                    SELECT DATETIME2FROMPARTS(2018, 2, 1, 1, 2, 3, 456, 3);
                    -- Result: 2018-02-01 01:02:03.456
                  

DATETIMEFROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute, integer_seconds, integer_milliseconds)

Returns the datetime value for the specified date parts.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• milliseconds: The integer expression specifying the milliseconds.

                    SELECT DATETIMEFROMPARTS(2018, 2, 1, 1, 2, 3, 456);
                    -- Result: 2018-02-01 01:02:03.456
                  

DATE_TRUNC(date, datepart)

Truncates the date to the precision of the given date part. Modeled after the Oracle TRUNC function.

• date: The datetime string that specifies the date.
• datepart: Refer to the Oracle documentation for valid datepart syntax.

				    SELECT DATE_TRUNC('05-04-2005', 'YY');
                    -- Result: '1/1/2005'
					
                    SELECT DATE_TRUNC('05-04-2005', 'MM');
                    -- Result: '5/1/2005'                    
                  

DATE_TRUNC2(datepart, date, [weekday])

Truncates the date to the precision of the given date part. Modeled after the PostgreSQL date_trunc function.

• datepart: One of 'millennium', 'century', 'decade', 'year', 'quarter', 'month', 'week', 'day', 'hour', 'minute' or 'second'.
• date: The datetime string that specifies the date.
• weekday: The optional day of the week to use as the first day for 'week'. One of 'sunday', 'monday', etc.

                    SELECT DATE_TRUNC2('year', '2020-02-04');
                    -- Result: '2020-01-01'

                    SELECT DATE_TRUNC2('week', '2020-02-04', 'monday');
                    -- Result: '2020-02-02', which is the previous Monday
                  

DAY(date)

Returns the integer that specifies the day component of the specified date.

• date: The datetime string that specifies the date.

                    SELECT DAY('2018-02-01');
                    -- Result: 1
                  

DAYOFMONTH(date)

• date: The datetime string that specifies the date.

				  SELECT DAYOFMONTH('04/15/2000');
				  -- Result: 15
				  

DAYOFWEEK(date)

• date: The datetime string that specifies the date.

				  SELECT DAYOFWEEK('04/15/2000');
				  -- Result: 7
				  

DAYOFYEAR(date)

• date: The datetime string that specifies the date.

				  SELECT DAYOFYEAR('04/15/2000');
				  -- Result: 106
				  

EOMONTH(date [, integer_month_to_add ]) or LAST_DAY(date)

Returns the last day of the month that contains the specified date with an optional offset.

• date: The datetime expression specifying the date for which to return the last day of the month.
• integer_month_to_add: The optional integer expression specifying the number of months to add to the date before calculating the end of the month.

                  SELECT EOMONTH('2018-02-01');
                  -- Result: 2018-02-28
                  
                  SELECT LAST_DAY('2018-02-01');
                  -- Result: 2018-02-28

                  SELECT EOMONTH('2018-02-01', 2);
                  -- Result: 2018-04-30
                

FDWEEK(date)

• date: The datetime string that specifies the date.

				  SELECT FDWEEK('02-08-2018');
				  -- Result: 2/4/2018
				  

FDMONTH(date)

• date: The datetime string that specifies the date.

				  SELECT FDMONTH('02-08-2018');
				  -- Result: 2/1/2018
				  

FDQUARTER(date)

• date: The datetime string that specifies the date.

				  SELECT FDQUARTER('05-08-2018');
				  -- Result: 4/1/2018
				  

FILEMODIFIEDTIME(uri)

Returns the time stamp associated with the Date Modified of the relevant file.

• uri: An absolute path pointing to a file on the local file system.

				 SELECT FILEMODIFIEDTIME('C:/Documents/myfile.txt');
				 -- Result: 6/25/2019 10:06:58 AM
				 

FROM_DAYS(datevalue)

Returns a date derived from the number of days after 1582-10-15 (based upon the Gregorian calendar). This will be equivalent to the MYSQL FROM_DAYS function.

• datevalue: A integer value representing the number of days since 1582-10-15.

				SELECT FROM_DAYS(736000);
				-- Result: 2/6/2015
				

GETDATE()

Returns the current time stamp of the database system as a datetime value. This value is equal to CURRENT_TIMESTAMP and SYSDATETIME, and is always in the local timezone.

                  SELECT GETDATE();
                  -- Result: 2018-02-01 03:04:05
                

GETUTCDATE()

Returns the current time stamp of the database system formatted as a UTC datetime value. This value is equal to SYSUTCDATETIME.

                  SELECT GETUTCDATE();
                  -- For example, if the local timezone is Eastern European Time (GMT+2)
                  -- Result: 2018-02-01 05:04:05
                

HOUR(date)

Returns the hour component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT HOUR('02-02-2020 11:30:00');
				-- Result: 11
				

ISDATE(date, [date_format])

Returns 1 if the value is a valid date, time, or datetime value; otherwise, 0.

• date: The datetime string.
• date_format: The optional datetime format.

                      SELECT ISDATE('2018-02-01', 'yyyy-MM-dd');
                      -- Result: 1

                      SELECT ISDATE('Not a date');
                      -- Result: 0
                    

LAST_WEEK()

Returns a time stamp equivalent to exactly one week before the current date.

				SELECT LAST_WEEK();	//Assume the date is 3/17/2020	
				-- Result: 3/10/2020
				

LAST_MONTH()

Returns a time stamp equivalent to exactly one month before the current date.

				SELECT LAST_MONTH(); //Assume the date is 3/17/2020	
				-- Result: 2/17/2020
				

LAST_YEAR()

Returns a time stamp equivalent to exactly one year before the current date.

				SELECT LAST_YEAR();	//Assume the date is 3/17/2020	
				-- Result: 3/10/2019
				

LDWEEK(date)

Returns the last day of the provided week.

• date: The datetime string.

				SELECT LDWEEK('02-02-2020');
				-- Result: 2/8/2020
				

LDMONTH(date)

Returns the last day of the provided month.

• date: The datetime string.

				SELECT LDMONTH('02-02-2020');
				-- Result: 2/29/2020
				

LDQUARTER(date)

Returns the last day of the provided quarter.

• date: The datetime string.

				SELECT LDQUARTER('02-02-2020');
				-- Result: 3/31/2020
				

MAKEDATE(year, days)

Returns a date value from a year and a number of days.

• year: The year
• days: The number of days into the year. Value must be greater than 0.

          SELECT MAKEDATE(2020, 1);
          -- Result: 2020-01-01
        

MINUTE(date)

Returns the minute component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT MINUTE('02-02-2020 11:15:00');
				-- Result: 15
				

MONTH(date)

Returns the month component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT MONTH('02-02-2020');
				-- Result: 2
				

QUARTER(date)

Returns the quarter associated with the provided datetime.

• date: The datetime string that specifies the date.

				SELECT QUARTER('02-02-2020');
				-- Result: 1
				

SECOND(date)

Returns the second component from the provided datetime.

• date: The datetime string that specifies the date.

				SELECT SECOND('02-02-2020 11:15:23');
				-- Result: 23
				

SMALLDATETIMEFROMPARTS(integer_year, integer_month, integer_day, integer_hour, integer_minute)

Returns the datetime value for the specified date and time.

• year: The integer expression specifying the year.
• month: The integer expression specifying the month.
• day: The integer expression specifying the day.
• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.

                      SELECT SMALLDATETIMEFROMPARTS(2018, 2, 1, 1, 2);
                      -- Result: 2018-02-01 01:02:00
                    

STRTODATE(string,format)

Parses the provided string value and returns the corresponding datetime.

• string: The string value to be converted to datetime format.
• format: A format string which describes how to interpret the first string input. A few special formats are available as well, including UNIX, UNIXMILIS, TICKS, and FILETICKS.

				SELECT STRTODATE('03*04*2020','dd*MM*yyyy');
				-- Result: 4/3/2020
				

SYSDATETIME()

Returns the current time stamp as a datetime value of the database system. It is equal to GETDATE and CURRENT_TIMESTAMP, and is always in the local timezone.

                  SELECT SYSDATETIME();
                  -- Result: 2018-02-01 03:04:05
                

SYSUTCDATETIME()

Returns the current system date and time as a UTC datetime value. It is equal to GETUTCDATE.

                  SELECT SYSUTCDATETIME();
                  -- For example, if the local timezone is Eastern European Time (GMT+2)
                  -- Result: 2018-02-01 05:04:05
                

TIMEFROMPARTS(integer_hour, integer_minute, integer_seconds, integer_fractions, integer_precision)

Returns the time value for the specified time and with the specified precision.

• hour: The integer expression specifying the hour.
• minute: The integer expression specifying the minute.
• seconds: The integer expression specifying the seconds.
• fractions: The integer expression specifying the fractions of the second.
• precision : The integer expression specifying the precision of the fraction.

                      SELECT TIMEFROMPARTS(1, 2, 3, 456, 3);
                      -- Result: 01:02:03.456
                    

TO_DAYS(date)

Returns the number of days since 0000-00-01. This will only return a value for dates on or after 1582-10-15 (based upon the Gregorian calendar). This will be equivalent to the MYSQL TO_DAYS function.

• date: The datetime string that specifies the date.

				SELECT TO_DAYS('02-06-2015');
				-- Result: 736000
				

WEEK(date)

Returns the week (of the year) associated with the provided datetime.

• date: The datetime string that specifies the date.

				SELECT WEEK('02-17-2020 11:15:23');
				-- Result: 8
				

YEAR(date)

Returns the integer that specifies the year of the specified date.

• date: The datetime string.

                      SELECT YEAR('2018-02-01');
                      -- Result: 2018
                    

ABS ( numeric_expression )

Returns the absolute (positive) value of the specified numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT ABS(15);
                      -- Result: 15

                      SELECT ABS(-15);
                      -- Result: 15
                    

ACOS ( float_expression )

Returns the arc cosine, the angle in radians whose cosine is the specified float expression.

• float_expression: The float expression that specifies the cosine of the angle to be returned. Values outside the range from -1 to 1 return null.

                      SELECT ACOS(0.5);
                      -- Result: 1.0471975511966
                    

ASIN ( float_expression )

Returns the arc sine, the angle in radians whose sine is the specified float expression.

• float_expression: The float expression that specifies the sine of the angle to be returned. Values outside the range from -1 to 1 return null.

                      SELECT ASIN(0.5);
                      -- Result: 0.523598775598299
                    

ATAN ( float_expression )

Returns the arc tangent, the angle in radians whose tangent is the specified float expression.

• float_expression: The float expression that specifies the tangent of the angle to be returned.

                      SELECT ATAN(10);
                      -- Result: 1.47112767430373
                    

ATN2 ( float_expression1 , float_expression2 )

Returns the angle in radians between the positive x-axis and the ray from the origin to the point (y, x) where x and y are the values of the two specified float expressions.

• float_expression1: The float expression that is the y-coordinate.
• float_expression2: The float expression that is the x-coordinate.

                      SELECT ATN2(1, 1);
                      -- Result: 0.785398163397448
                    

CEILING ( numeric_expression ) or CEIL( numeric_expression )

Returns the smallest integer greater than or equal to the specified numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT CEILING(1.3);
                      -- Result: 2

                      SELECT CEILING(1.5);
                      -- Result: 2

                      SELECT CEILING(1.7);
                      -- Result: 2
                    

COS ( float_expression )

Returns the trigonometric cosine of the specified angle in radians in the specified expression.

• float_expression: The float expression of the specified angle in radians.

                      SELECT COS(1);
                      -- Result: 0.54030230586814
                    

COT ( float_expression )

Returns the trigonometric cotangent of the angle in radians specified by float_expression.

• float_expression: The float expression of the angle in radians.

                      SELECT COT(1);
                      -- Result: 0.642092615934331
                    

DEGREES ( numeric_expression )

Returns the angle in degrees for the angle specified in radians.

• numeric_expression: The angle in radians, an expression of an indeterminate numeric data type except for the bit data type.

                      SELECT DEGREES(3.1415926);
                      -- Result: 179.999996929531
                    

EXP ( float_expression )

Returns the exponential value of the specified float expression. For example, EXP(LOG(20)) is 20.

• float_expression: The float expression.

                      SELECT EXP(2);
                      -- Result: 7.38905609893065
                    

EXPR ( expression )

Evaluates the expression.

• expression: The expression. Operators allowed are +, -, *, /, ==, !=, >, <, >=, and <=.

                      SELECT EXPR('1 + 2 * 3');
                      -- Result: 7

                      SELECT EXPR('1 + 2 * 3 == 7');
                      -- Result: true
                    

FLOOR ( numeric_expression )

Returns the largest integer less than or equal to the numeric expression.

• numeric_expression: The expression of an indeterminate numeric data type except for the bit data type.

                      SELECT FLOOR(1.3);
                      -- Result: 1

                      SELECT FLOOR(1.5);
                      -- Result: 1

                      SELECT FLOOR(1.7);
                      -- Result: 1
                    

GREATEST(int1,int2,....)

Returns the greatest of the supplied integers.

				SELECT GREATEST(3,5,8,10,1)
				-- Result: 10			
				

HEX(value)

Returns a the equivalent hex for the input value.

• value: A string or numerical value to be converted into hex.

				SELECT HEX(866849198);
				-- Result: 33AB11AE
				
				SELECT HEX('Sample Text');
				-- Result: 53616D706C652054657874
				

LEAST(int1,int2,....)

Returns the least of the supplied integers.

				SELECT LEAST(3,5,8,10,1)
				-- Result: 1			
				

LOG ( float_expression [, base ] )

Returns the natural logarithm of the specified float expression.

• float_expression: The float expression.
• base: The optional integer argument that sets the base for the logarithm.

                      SELECT LOG(7.3890560);
                      -- Result: 1.99999998661119
                    

LOG10 ( float_expression )

Returns the base-10 logarithm of the specified float expression.

• float_expression: The expression of type float.

                      SELECT LOG10(10000);
                      -- Result: 4
                    

MOD(dividend,divisor)

Returns the integer value associated with the remainder when dividing the dividend by the divisor.

• dividend: The number to take the modulus of.
• divisor: The number to divide the dividend by when determining the modulus.

				SELECT MOD(10,3);
				-- Result: 1
				

NEGATE(real_number)

Returns the opposite to the real number input.

• real_number: The real number to find the opposite of.

				SELECT NEGATE(10);
				-- Result: -10
				
				SELECT NEGATE(-12.4)
				--Result: 12.4
				

PI ( )

Returns the constant value of pi.

                  SELECT PI()
                  -- Result: 3.14159265358979 
                

POWER ( float_expression , y )

Returns the value of the specified expression raised to the specified power.

• float_expression: The float expression.
• y: The power to raise float_expression to.

                      SELECT POWER(2, 10);
                      -- Result: 1024

                      SELECT POWER(2, -2);
                      -- Result: 0.25
                    

RADIANS ( float_expression )

Returns the angle in radians of the angle in degrees.

• float_expression: The degrees of the angle as a float expression.

                      SELECT RADIANS(180);
                      -- Result: 3.14159265358979
                    

RAND ( [ integer_seed ] )

Returns a pseudorandom float value from 0 through 1, exclusive.

• seed: The optional integer expression that specifies the seed value. If seed is not specified, a seed value at random will be assigned.

                      SELECT RAND();
                      -- This result may be different, since the seed is randomized
                      -- Result: 0.873159630165044

                      SELECT RAND(1);
                      -- This result will always be the same, since the seed is constant
                      -- Result: 0.248668584157093
                    

ROUND ( numeric_expression [ ,integer_length] [ ,function ] )

Returns the numeric value rounded to the specified length or precision.

• numeric_expression: The expression of a numeric data type.
• length: The optional precision to round the numeric expression to. When this is ommitted, the default behavior will be to round to the nearest whole number.
• function: The optional type of operation to perform. When the function parameter is omitted or has a value of 0 (default), numeric_expression is rounded. When a value other than 0 is specified, numeric_expression is truncated.

                      SELECT ROUND(1.3, 0);
                      -- Result: 1

                      SELECT ROUND(1.55, 1);
                      -- Result: 1.6

                      SELECT ROUND(1.7, 0, 0);
                      -- Result: 2

                      SELECT ROUND(1.7, 0, 1);
                      -- Result: 1
                      
                      SELECT ROUND (1.24);
                      -- Result: 1.0
                    

SIGN ( numeric_expression )

Returns the positive sign (1), 0, or negative sign (-1) of the specified expression.

• numeric_expression: The expression of an indeterminate data type except for the bit data type.

                      SELECT SIGN(0);
                      -- Result: 0

                      SELECT SIGN(10);
                      -- Result: 1

                      SELECT SIGN(-10);
                      -- Result: -1
                    

SIN ( float_expression )

Returns the trigonometric sine of the angle in radians.

• float_expression: The float expression specifying the angle in radians.

                     SELECT SIN(1);
                     -- Result: 0.841470984807897
                    

SQRT ( float_expression )

Returns the square root of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQRT(100);
                      -- Result: 10
                    

SQUARE ( float_expression )

Returns the square of the specified float value.

• float_expression: The expression of type float.

                      SELECT SQUARE(10);
                      -- Result: 100

                      SELECT SQUARE(-10);
                      -- Result: 100
                    

TAN ( float_expression )

Returns the tangent of the input expression.

• float_expression: The expression of type float.

                      SELECT TAN(1);
                      -- Result: 1.5574077246549
                    

TRUNC(decimal_number,precision)

Returns the supplied decimal number truncated to have the supplied decimal precision.

• decimal_number: The decimal value to truncate.
• precision: The number of decimal places to truncate the decimal number to.

				SELECT TRUNC(10.3423,2);
				-- Result: 10.34
				

To create new records, use INSERT statements.

INSERT Syntax

The INSERT statement specifies the columns to be inserted and the new column values. You can specify the column values in a comma-separated list in the VALUES clause, as shown in the following example:

INSERT INTO <table_name> 
( <column_reference> [ , ... ] )
VALUES 
( { <expression> | NULL } [ , ... ] ) 
  

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

INSERT INTO [CData].[Sample].Customers (CompanyName) VALUES ('Caterpillar')

To modify existing records, use UPDATE statements.

Update Syntax

The UPDATE statement takes as input a comma-separated list of columns and new column values as name-value pairs in the SET clause, as shown in the following example:

UPDATE <table_name> SET { <column_reference> = <expression> } [ , ... ] WHERE { _id = <expression>  } [ { AND | OR } ... ] 

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

The following is an example query:

UPDATE [CData].[Sample].Customers SET CompanyName='Caterpillar' WHERE _id = @my_id

To delete information from a table, use DELETE statements.

DELETE Syntax

The DELETE statement requires the table name in the FROM clause and the row's primary key in the WHERE clause, as shown in the following example:

<delete_statement> ::= DELETE FROM <table_name> WHERE { _id = <expression> } [ { AND | OR } ... ]

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

The following is an example query:

DELETE FROM [CData].[Sample].Customers WHERE _id = @my_id

To execute stored procedures, you can use EXECUTE or EXEC statements.

EXEC and EXECUTE assign stored procedure inputs, referenced by name, to values or parameter names.

Stored Procedure Syntax

To execute a stored procedure as an SQL statement, use the following syntax:

 
{ EXECUTE | EXEC } <stored_proc_name> 
{
  [ @ ] <input_name> = <expression>
} [ , ... ]

<expression> ::=
  | @ <parameter> 
  | ?
  | <literal>

Example Statements

Reference stored procedure inputs by name:

EXECUTE my_proc @second = 2, @first = 1, @third = 3;

Execute a parameterized stored procedure statement:

EXECUTE my_proc second = @p1, first = @p2, third = @p3; 

PIVOT and UNPIVOT can be used to change a table-valued expression into another table.

PIVOT

PIVOT Synax

 
"SELECT 'AverageCost' AS Cost_Sorted_By_Production_Days, [0], [1], [2], [3], [4]
FROM
(
SELECT DaysToManufacture, StandardCost
FROM Production.Product
) AS SourceTable
PIVOT
(
AVG(StandardCost)
FOR DaysToManufacture IN ([0], [1], [2], [3], [4])
) AS PivotTable;"

UNPIVOT

UNPIVOT Sytax

 
"SELECT VendorID, Employee, Orders
FROM
(SELECT VendorID, Emp1, Emp2, Emp3, Emp4, Emp5
FROM pvt) p
UNPIVOT
(Orders FOR Employee IN
(Emp1, Emp2, Emp3, Emp4, Emp5)
)AS unpvt;"

For further information on PIVOT and UNPIVOT, see FROM clause plus JOIN, APPLY, PIVOT (Transact-SQL)

To perform multiple inserts in a single request to MongoDB, use the INSERT INTO SELECT syntax to insert a temporary table of data into MongoDB. This works by first populating a temporary table with the data you are going to submit to MongoDB. Once you have all of the data you want to insert, the temporary table is then passed into the table in MongoDB.

Populate the Temporary Table

The temporary table you are populating is dynamic and is created at run time the first time you insert to it. Temporary tables are denoted by a # appearing in their name. When using a temporary table to insert, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you will be inserting to. For example:

INSERT INTO [CData].[Sample].Customers#TEMP (CompanyName, MyCustomField__c) VALUES ('New Customers', '9000');
INSERT INTO [CData].[Sample].Customers#TEMP (CompanyName, MyCustomField__c) VALUES ('New Customers 2', '9001');
INSERT INTO [CData].[Sample].Customers#TEMP (CompanyName, MyCustomField__c) VALUES ('New Customers 3', '9002');

This creates a temporary table called [CData].[Sample].Customers#TEMP with two columns and three rows of data. Since type cannot be determined on the temporary table itself, all values are stored in memory as strings. They are later converted to the proper type when they are submitted to the [CData].[Sample].Customers table.

Insert to the Actual Table

Once your temporary table is populated, it is now time to insert to the actual table in MongoDB. You can do this by performing an INSERT to the actual table and selecting the input data from the temporary table. For example:

INSERT INTO [CData].[Sample].Customers (CompanyName, MyCustomField__c) SELECT CompanyName, MyCustomField__c FROM [CData].[Sample].Customers#TEMP

Results

The results of the query are stored in the LastResultInfo#TEMP temporary table. This table is cleared and repopulated the next time data is modified by passing in a temporary table. Please be aware that the LastResultInfo#TEMP table has no predefined schema. You need to check its metadata at run time before reading data.

Temporary Table Life Span

Temporary tables only last as long as the connection remains open. When the connection to MongoDB is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

To perform multiple updates in a single request to MongoDB,first use the INSERT INTO syntax to insert a temporary table of data into MongoDB. This works by first populating a temporary table with the data you are going to submit to MongoDB. Once you have all of the data you want to update, use UPDATE SELECT FROM to pass the temporary table data into the table in MongoDB.

Populate the Temporary Table

The temporary table you are populating is dynamic and is created at run time the first time you insert to it. Temporary tables are denoted by a # appearing in their name. When using a temporary table to update, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you are inserting to. For example:

INSERT INTO [CData].[Sample].Customers#TEMP (_id, Name, MyCustomField__c) VALUES ('AX1000001', 'New Customers', '9000');
INSERT INTO [CData].[Sample].Customers#TEMP (_id, Name, MyCustomField__c) VALUES ('AX1000002', 'New Customers 2', '9001');
INSERT INTO [CData].[Sample].Customers#TEMP (_id, Name, MyCustomField__c) VALUES ('AX1000003', 'New Customers 3', '9002');

This creates a temporary table called [CData].[Sample].Customers#TEMP with three columns and three rows of data. Since type cannot be determined on the temporary table itself, all values are stored in memory as strings. The values are later converted to the proper type when they are submitted to the [CData].[Sample].Customers table.

Update the Actual Table

Once your temporary table is populated, it is now time to update the actual table in MongoDB. You can do this by performing an UPDATE to the actual table and selecting the input data from the temporary table. For example:

UPDATE [CData].[Sample].Customers (_id, CompanyName, MyCustomField__c) SELECT _id, CompanyName, MyCustomField__c FROM [CData].[Sample].Customers#TEMP

Results

The results of the query are stored in the LastResultInfo#TEMP temporary table. This table is cleared and repopulated the next time data is modified by passing in a temporary table. Please be aware that the LastResultInfo#TEMP table has no predefined schema. You need to check its metadata at run time before reading data.

Temporary Table Life Span

Temporary tables only last as long as the connection remains open. When the connection to MongoDB is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

To perform multiple deletes in a single request to MongoDB, first use the INSERT INTO syntax to create an in-memory temporary table of data to be deleted. Once you have all of the data you want to delete added to temporary table, use DELETE FROM syntax to delete data from the live table in MongoDB. This functionality is also available via the standard Batch Processing API available in JDBC.

Populate the Temporary Table

The temporary table you are populating is dynamic and is created at run time the first time you insert to it. Temporary tables are denoted by a # appearing in their name. When using a temporary table to delete, the temporary table must be named in the format [TableName]#TEMP, where TableName is the name of the table you are inserting to. For example:

INSERT INTO [CData].[Sample].Customers#TEMP (_id) VALUES ('AX1000001');
INSERT INTO [CData].[Sample].Customers#TEMP (_id) VALUES ('AX1000002');
INSERT INTO [CData].[Sample].Customers#TEMP (_id) VALUES ('AX1000003');

This creates a temporary table called [CData].[Sample].Customers#TEMP with one column and three rows of data. Since type cannot be determined on the temporary table itself, all values are stored in memory as strings. They are later converted to the proper type when they are submitted to the [CData].[Sample].Customers table.

Delete from the Actual Table

Once your temporary table is populated, it is now time to insert to the actual table in MongoDB. You can do this by performing a DELETE from the actual table and selecting the input data from the temporary table. For example:

DELETE FROM [CData].[Sample].Customers WHERE EXISTS SELECT _id FROM [CData].[Sample].Customers#TEMP

In this example, the full contents of the [CData].[Sample].Customers#TEMP table are passed into the [CData].[Sample].Customers table. This results in fewer requests being submitted to MongoDB since multiple deletes may be submitted with each request, which is much better for performance if you have many records to delete.

Results

The results of the query are stored in the LastResultInfo#TEMP temporary table. This table is cleared and repopulated the next time data is modified by passing in a temporary table. Please be aware that the LastResultInfo#TEMP table has no predefined schema. You need to check its metadata at run time before reading data.

Temporary Table Life Span

Temporary tables only last as long as the connection remains opened. When the connection to MongoDB is closed, all temporary tables are cleared, including the LastResultInfo#TEMP table.

To create new MongoDB entities, use CREATE TABLE statements.

CREATE TABLE Syntax

The CREATE TABLE statement specifies the table name and a comma-separated list of column names and the primary keys of the table, as shown in the following example:

CREATE TABLE <table_name> [ IF [ NOT EXISTS ] ]
( 
  { 
     <column_name> <data_type> 
     [ NOT NULL ] 
     [ DEFAULT <literal> ] 
     [ PRIMARY KEY ] 
     [ UNIQUE ] 
  } |  PRIMARY KEY ( <column_name> [ , ... ] ) 
  [ , ... ]
)

The following example statement creates a MyCustomers table on the MongoDB server with name, age, and address columns:

CREATE TABLE IF NOT EXISTS [MyCustomers] (name VARCHAR(20), age INT, address VARCHAR(20))

Use DROP TABLE statements to delete a table and all the data it contains from MongoDB.

DROP TABLE Syntax

The DROP TABLE statement accepts the name of the table to delete, as shown in the following example:

DROP TABLE [ IF EXISTS ] <table_name> 

The following query deletes all MyCustomers data from the server:

DROP TABLE IF EXISTS MyCustomers

The connection string properties are the various options that can be used to establish a connection. This section provides a complete list of the options you can configure in the connection string for this provider. Click the links for further details.

Authentication

Kerberos

SSL

SSH

Firewall

Logging

Schema

Caching

Miscellaneous

This section provides a complete list of the Authentication properties you can configure in the connection string for this provider.

The authentication mechanism that MongoDB will use to authenticate the connection.

Possible Values

Data Type

string

Default Value

"NONE"

Remarks

Accepted values are MONGODB-CR, SCRAM-SHA-1, SCRAM-SHA-256, GSSAPI, PLAIN, and NONE. The following authentication types correspond to the authentication values.

Authenticating with Challenge-Response

Generally, this property does not need to be set for this authentication type, as the Cloud uses different challenge-response mechanisms by default to authenticate a user to different versions of MongoDB.

• MongoDB 2: MongoDB 2 uses MONGODB-CR to authenticate.
• MongoDB 3.x: MongoDB 3 uses SCRAM-SHA-1 by default; new users you create in MongoDB 3 use this authentication method. However, MongoDB 3 servers will continue to use MONGODB-CR to authenticate users created in MongoDB 2.6.
• MongoDB 4.x: MongoDB 4 uses SCRAM-SHA-1 by default and does not support the deprecated MongoDB MONGODB-CR authentication mechanism.

Authenticating with LDAP

Set AuthScheme to PLAIN to use LDAP authentication. This value specifies the SASL PLAIN mechanism; note that this mechanism transmits credentials over plain-text, so it is not suitable for use without TLS/SSL on untrusted networks.

Authenticating with Kerberos

Set AuthScheme to GSSAPI to use Kerberos authentication. Additionally configure the following properties as configured for the MongoDB environment:

Authenticating with X.509 Authentication

Set AuthScheme to X509 to use X.509 certificate authentication.

The host name or IP address of the server hosting the MongoDB database.

Data Type

string

Default Value

""

Remarks

The host name or IP address of the server hosting the MongoDB database. If you choose to connect using DNS seed lists, set this option to "mongodb+srv://" + the name of the server your MongoDB instance is running on..

The port for the MongoDB database.

Data Type

string

Default Value

"27017"

Remarks

The port for the MongoDB database.

The MongoDB user account used to authenticate.

Data Type

string

Default Value

""

Remarks

Together with Password, this field is used to authenticate against the MongoDB server.

The password used to authenticate the user.

Data Type

string

Default Value

""

Remarks

The User and Password are together used to authenticate with the server.

The name of the MongoDB database.

Data Type

string

Default Value

""

Remarks

The name of the MongoDB database.

This field sets whether SSL is enabled.

Data Type

bool

Default Value

false

Remarks

This field sets whether the Cloud will attempt to negotiate TLS/SSL connections to the server. By default, the Cloud checks the server's certificate against the system's trusted certificate store. To specify another certificate, set SSLServerCert.

The name of the MongoDB database for authentication.

Data Type

string

Default Value

""

Remarks

The name of the MongoDB database for authentication. Only needed if the authentication database is different from the database to retrieve data from.

This property allows you to specify multiple servers in addition to the one configured in Server and Port . Specify both a server name and port; separate servers with a comma.

Data Type

string

Default Value

""

Remarks

This property allows you to specify the other servers in the replica set in addition to the one configured in Server and Port. You must specify all servers in the replica set using ReplicaSet, Server, and Port.

Specify both a server name and port in ReplicaSet; separate servers with a comma. For example:

Server=localhost;Port=27017;ReplicaSet=localhost:27018,localhost:27019;

To find the primary server, the Cloud queries the servers in ReplicaSet and the server specified by Server and Port.

Note that only the primary server in a replica set is writable. Secondaries can be readable if the SlaveOK setting allows it. To configure a strategy executing SELECT queries to secondaries, see ReadPreference.

Specify the DNS server when resolving MongoDB seed list.

Data Type

string

Default Value

""

Remarks

Specify the DNS server when resolving MongoDB seed list.

This section provides a complete list of the Kerberos properties you can configure in the connection string for this provider.

The Kerberos Key Distribution Center (KDC) service used to authenticate the user.

Data Type

string

Default Value

""

Remarks

The Kerberos properties are used when using SPNEGO or Windows Authentication. The Cloud will request session tickets and temporary session keys from the Kerberos KDC service. The Kerberos KDC service is conventionally colocated with the domain controller.

If Kerberos KDC is not specified, the Cloud will attempt to detect these properties automatically from the following locations:

• KRB5 Config File (krb5.ini/krb5.conf): If the KRB5_CONFIG environment variable is set and the file exists, the Cloud will obtain the KDC from the specified file. Otherwise, it will attempt to read from the default MIT location based on the OS: C:\ProgramData\MIT\Kerberos5\krb5.ini (Windows) or /etc/krb5.conf (Linux).
• Domain Name and Host: If the Kerberos Realm and Kerberos KDC could not be inferred from another location, the Cloud will infer them from the configured domain name and host.

The Kerberos Realm used to authenticate the user.

Data Type

string

Default Value

""

Remarks

The Kerberos properties are used when using SPNEGO or Windows Authentication. The Kerberos Realm is used to authenticate the user with the Kerberos Key Distribution Service (KDC). The Kerberos Realm can be configured by an administrator to be any string, but conventionally it is based on the domain name.

If Kerberos Realm is not specified, the Cloud will attempt to detect these properties automatically from the following locations:

• KRB5 Config File (krb5.ini/krb5.conf): If the KRB5_CONFIG environment variable is set and the file exists, the Cloud will obtain the default realm from the specified file. Otherwise, it will attempt to read from the default MIT location based on the OS: C:\ProgramData\MIT\Kerberos5\krb5.ini (Windows) or /etc/krb5.conf (Linux)
• Domain Name and Host: If the Kerberos Realm and Kerberos KDC could not be inferred from another location, the Cloud will infer them from the user-configured domain name and host. This might work in some Windows environments.

The service principal name (SPN) for the Kerberos Domain Controller.

Data Type

string

Default Value

""

Remarks

If the SPN on the Kerberos Domain Controller is not the same as the URL that you are authenticating to, use this property to set the SPN.

The Keytab file containing your pairs of Kerberos principals and encrypted keys.

Data Type

string

Default Value

""

Remarks

The Keytab file containing your pairs of Kerberos principals and encrypted keys.

The Kerberos realm of the service.

Data Type

string

Default Value

""

Remarks

The KerberosServiceRealm is the specify the service Kerberos realm when using cross-realm Kerberos authentication.

In most cases, a single realm and KDC machine are used to perform the Kerberos authentication and this property is not required.

This property is available for complex setups where a different realm and KDC machine are used to obtain an authentication ticket (AS request) and a service ticket (TGS request).

The Kerberos KDC of the service.

Data Type

string

Default Value

""

Remarks

The KerberosServiceKDC is used to specify the service Kerberos KDC when using cross-realm Kerberos authentication.

In most cases, a single realm and KDC machine are used to perform the Kerberos authentication and this property is not required.

This property is available for complex setups where a different realm and KDC machine are used to obtain an authentication ticket (AS request) and a service ticket (TGS request).

The full file path to an MIT Kerberos credential cache file.

Data Type

string

Default Value

""

Remarks

This property can be set if you wish to use a credential cache file that was created using the MIT Kerberos Ticket Manager or kinit command.

This section provides a complete list of the SSL properties you can configure in the connection string for this provider.

The TLS/SSL client certificate store for SSL Client Authentication (2-way SSL).

Data Type

string

Default Value

""

Remarks

The name of the certificate store for the client certificate.

The SSLClientCertType field specifies the type of the certificate store specified by SSLClientCert. If the store is password protected, specify the password in SSLClientCertPassword.

SSLClientCert is used in conjunction with the SSLClientCertSubject field in order to specify client certificates. If SSLClientCert has a value, and SSLClientCertSubject is set, a search for a certificate is initiated. See SSLClientCertSubject for more information.

Designations of certificate stores are platform-dependent.

The following are designations of the most common User and Machine certificate stores in Windows:

In Java, the certificate store normally is a file containing certificates and optional private keys.

When the certificate store type is PFXFile, this property must be set to the name of the file. When the type is PFXBlob, the property must be set to the binary contents of a PFX file (for example, PKCS12 certificate store).

The type of key store containing the TLS/SSL client certificate.

Possible Values

Data Type

string

Default Value

"USER"

Remarks

This property can take one of the following values:

The password for the TLS/SSL client certificate.

Data Type

string

Default Value

""

Remarks

If the certificate store is of a type that requires a password, this property is used to specify that password to open the certificate store.

The subject of the TLS/SSL client certificate.

Data Type

string

Default Value

"*"

Remarks

When loading a certificate the subject is used to locate the certificate in the store.

If an exact match is not found, the store is searched for subjects containing the value of the property. If a match is still not found, the property is set to an empty string, and no certificate is selected.

The special value "*" picks the first certificate in the certificate store.

The certificate subject is a comma separated list of distinguished name fields and values. For example, "CN=www.server.com, OU=test, C=US, [email protected]". The common fields and their meanings are shown below.

If a field value contains a comma, it must be quoted.

The certificate to be accepted from the server when connecting using TLS/SSL.

Data Type

string

Default Value

""

Remarks

If using a TLS/SSL connection, this property can be used to specify the TLS/SSL certificate to be accepted from the server. Any other certificate that is not trusted by the machine is rejected.

This property can take the following forms:

If not specified, any certificate trusted by the machine is accepted.

Use '*' to signify to accept all certificates. Note that this is not recommended due to security concerns.

This section provides a complete list of the SSH properties you can configure in the connection string for this provider.

The authentication method to be used to log on to an SFTP server.

Possible Values

Data Type

string

Default Value

"Password"

Remarks

• None: No authentication will be performed. The current User value is ignored, and the connection will be logged in as anonymous.
• Password: The Cloud will use the values of User and Password to authenticate the user.
• Public_Key: The Cloud will use the values of User and SSHClientCert to authenticate the user. SSHClientCert must have a private key available for this authentication method to succeed.

A private key to be used for authenticating the user.

Data Type

string

Default Value

""

Remarks

SSHClientCert must contain a valid private key in order to use public key authentication. A public key is optional, if one is not included then the Cloud generates it from the private key. The Cloud sends the public key to the server and the connection is allowed if the user has authorized the public key.

The SSHClientCertType field specifies the type of the key store specified by SSHClientCert. If the store is password protected, specify the password in SSHClientCertPassword.

Some types of key stores are containers which may include multiple keys. By default the Cloud will select the first key in the store, but you can specify a specific key using SSHClientCertSubject.

The password of the SSHClientCert key if it has one.

Data Type

string

Default Value

""

Remarks

This property is only used when authenticating to SFTP servers with SSHAuthMode set to PublicKey and SSHClientCert set to a private key.

The subject of the SSH client certificate.

Data Type

string

Default Value

"*"

Remarks

When loading a certificate the subject is used to locate the certificate in the store.

If an exact match is not found, the store is searched for subjects containing the value of the property.

If a match is still not found, the property is set to an empty string, and no certificate is selected.

The special value "*" picks the first certificate in the certificate store.

The certificate subject is a comma separated list of distinguished name fields and values. For instance "CN=www.server.com, OU=test, C=US, [email protected]". Common fields and their meanings are displayed below.

If a field value contains a comma it must be quoted.

The type of SSHClientCert private key.

Possible Values

Data Type

string

Default Value

"PEMKEY_FILE"

Remarks

This property can take one of the following values:

The SSH server.

Data Type

string

Default Value

""

Remarks

The SSH server.

The SSH port.

Data Type

string

Default Value

"22"

Remarks

The SSH port.

The SSH user.

Data Type

string

Default Value

""

Remarks

The SSH user.

The SSH password.

Data Type

string

Default Value

""

Remarks

The SSH password.

The SSH server fingerprint.

Data Type

string

Default Value

""

Remarks

The SSH server fingerprint.

Whether to tunnel the MongoDB connection over SSH. Use SSH.

Data Type

bool

Default Value

false

Remarks

By default the Cloud will attempt to connect directly to MongoDB. When this option is enabled, the Cloud will instead establish an SSH connection with the SSHServer and tunnel the connection to MongoDB through it.

This section provides a complete list of the Firewall properties you can configure in the connection string for this provider.

The protocol used by a proxy-based firewall.

Possible Values

Data Type

string

Default Value

"NONE"

Remarks

This property specifies the protocol that the Cloud will use to tunnel traffic through the FirewallServer proxy.

The name or IP address of a proxy-based firewall.

Data Type

string

Default Value

""

Remarks

This property specifies the IP address, DNS name, or host name of a proxy allowing traversal of a firewall. The protocol is specified by FirewallType: Use FirewallServer with this property to connect through SOCKS or do tunneling.

The TCP port for a proxy-based firewall.

Data Type

int

Default Value

0

Remarks

This specifies the TCP port for a proxy allowing traversal of a firewall. Use FirewallServer to specify the name or IP address. Specify the protocol with FirewallType.

The user name to use to authenticate with a proxy-based firewall.

Data Type

string

Default Value

""

Remarks

The FirewallUser and FirewallPassword properties are used to authenticate against the proxy specified in FirewallServer and FirewallPort, following the authentication method specified in FirewallType.

A password used to authenticate to a proxy-based firewall.

Data Type

string

Default Value

""

Remarks

This property is passed to the proxy specified by FirewallServer and FirewallPort, following the authentication method specified by FirewallType.

This section provides a complete list of the Logging properties you can configure in the connection string for this provider.

A filepath which designates the name and location of the log file.

Data Type

string

Default Value

""

Remarks

Once this property is set, the Cloud will populate the log file as it carries out various tasks, such as when authentication is performed or queries are executed. If the specified file doesn't already exist, it will be created.

Connection strings and version information are also logged, though connection properties containing sensitive information are masked automatically.

If a relative filepath is supplied, the location of the log file will be resolved based on the path found in the Location connection property.

For more control over what is written to the log file, you can adjust the Verbosity property.

Log contents are categorized into several modules. You can show/hide individual modules using the LogModules property.

To edit the maximum size of a single logfile before a new one is created, see MaxLogFileSize.

If you would like to place a cap on the number of logfiles generated, use MaxLogFileCount.

The verbosity level that determines the amount of detail included in the log file.

Data Type

string

Default Value

"1"

Remarks

The verbosity level determines the amount of detail that the Cloud reports to the Logfile. Verbosity levels from 1 to 5 are supported. These are detailed in the Logging page.

Core modules to be included in the log file.

Data Type

string

Default Value

""

Remarks

Only the modules specified (separated by ';') will be included in the log file. By default all modules are included.

See the Logging page for an overview.

A string specifying the maximum size in bytes for a log file (for example, 10 MB).

Data Type

string

Default Value

"100MB"

Remarks

When the limit is hit, a new log is created in the same folder with the date and time appended to the end. The default limit is 100 MB. Values lower than 100 kB will use 100 kB as the value instead.

Adjust the maximum number of logfiles generated with MaxLogFileCount.

A string specifying the maximum file count of log files.

Data Type

int

Default Value

-1

Remarks

When the limit is hit, a new log is created in the same folder with the date and time appended to the end and the oldest log file will be deleted.

The minimum supported value is 2. A value of 0 or a negative value indicates no limit on the count.

Adjust the maximum size of the logfiles generated with MaxLogFileSize.

This section provides a complete list of the Schema properties you can configure in the connection string for this provider.

A path to the directory that contains the schema files defining tables, views, and stored procedures.

Data Type

string

Default Value

"%APPDATA%\\CData\\MongoDB Data Provider\\Schema"

Remarks

The path to a directory which contains the schema files for the Cloud (.rsd files for tables and views, .rsb files for stored procedures). The folder location can be a relative path from the location of the executable. The Location property is only needed if you want to customize definitions (for example, change a column name, ignore a column, and so on) or extend the data model with new tables, views, or stored procedures.

If left unspecified, the default location is "%APPDATA%\\CData\\MongoDB Data Provider\\Schema" with %APPDATA% being set to the user's configuration directory:

This property restricts the schemas reported to a subset of the available schemas. For example, BrowsableSchemas=SchemaA,SchemaB,SchemaC.

Data Type

string

Default Value

""

Remarks

Listing the schemas from databases can be expensive. Providing a list of schemas in the connection string improves the performance.

This property restricts the tables reported to a subset of the available tables. For example, Tables=TableA,TableB,TableC.

Data Type

string

Default Value

""

Remarks

Listing the tables from some databases can be expensive. Providing a list of tables in the connection string improves the performance of the Cloud.

This property can also be used as an alternative to automatically listing views if you already know which ones you want to work with and there would otherwise be too many to work with.

Specify the tables you want in a comma-separated list. Each table should be a valid SQL identifier with any special characters escaped using square brackets, double-quotes or backticks. For example, Tables=TableA,[TableB/WithSlash],WithCatalog.WithSchema.`TableC With Space`.

Note that when connecting to a data source with multiple schemas or catalogs, you will need to provide the fully qualified name of the table in this property, as in the last example here, to avoid ambiguity between tables that exist in multiple catalogs or schemas.

Restricts the views reported to a subset of the available tables. For example, Views=ViewA,ViewB,ViewC.

Data Type

string

Default Value

""

Remarks

Listing the views from some databases can be expensive. Providing a list of views in the connection string improves the performance of the Cloud.

This property can also be used as an alternative to automatically listing views if you already know which ones you want to work with and there would otherwise be too many to work with.

Specify the views you want in a comma-separated list. Each view should be a valid SQL identifier with any special characters escaped using square brackets, double-quotes or backticks. For example, Views=ViewA,[ViewB/WithSlash],WithCatalog.WithSchema.`ViewC With Space`.

Note that when connecting to a data source with multiple schemas or catalogs, you will need to provide the fully qualified name of the table in this property, as in the last example here, to avoid ambiguity between tables that exist in multiple catalogs or schemas.

This section provides a complete list of the Caching properties you can configure in the connection string for this provider.

Automatically caches the results of SELECT queries into a cache database specified by either CacheLocation or both of CacheConnection and CacheProvider .

Data Type

bool

Default Value

false

Remarks

When AutoCache = true, the Cloud automatically maintains a cache of your table's data in the database of your choice.

Setting the Caching Database

When AutoCache = true, the Cloud caches to a simple, file-based cache. You can configure its location or cache to a different database with the following properties:

• CacheLocation: Specifies the path to the file store.
• CacheProvider and CacheConnection: Specifies a driver to a database and the connection string.

See Also

• CacheMetadata: This property reduces the amount of metadata that crosses the network by persisting table schemas retrieved from the MongoDB metadata. Metadata then needs to be retrieved only once instead of every connection.
• Explicitly Caching Data: This section provides more examples of using AutoCache in Offline mode.
• CACHE Statements: You can use the CACHE statement to persist any SELECT query, as well as manage the cache; for example, refreshing schemas.

Specifies the path to the cache when caching to a file.

Data Type

string

Default Value

"%APPDATA%\\CData\\MongoDB Data Provider"

Remarks

The CacheLocation is a simple, file-based cache.

If left unspecified, the default location is "%APPDATA%\\CData\\MongoDB Data Provider" with %APPDATA% being set to the user's configuration directory:

See Also

• AutoCache: Set to implicitly create and maintain a cache for later offline use.
• CacheMetadata: Set to persist the MongoDB catalog in CacheLocation.

The tolerance for stale data in the cache specified in seconds when using AutoCache .

Data Type

int

Default Value

600

Remarks

The tolerance for stale data in the cache specified in seconds. This only applies when AutoCache is used. The Cloud checks with the data source for newer records after the tolerance interval has expired. Otherwise, it returns the data directly from the cache.

Use offline mode to get the data from the cache instead of the live source.

Data Type

bool

Default Value

false

Remarks

When Offline = true, all queries execute against the cache as opposed to the live data source. In this mode, certain queries like INSERT, UPDATE, DELETE, and CACHE are not allowed.

This property determines whether or not to cache the table metadata to a file store.

Data Type

bool

Default Value

false

Remarks

As you execute queries with this property set, table metadata in the MongoDB catalog are cached to the file store specified by CacheLocation if set or the user's home directory otherwise. A table's metadata will be retrieved only once, when the table is queried for the first time.

When to Use CacheMetadata

The Cloud automatically persists metadata in memory for up to two hours when you first discover the metadata for a table or view and therefore, CacheMetadata is generally not required. CacheMetadata becomes useful when metadata operations are expensive such as when you are working with large amounts of metadata or when you have many short-lived connections.

When Not to Use CacheMetadata

• When you are working with volatile metadata: Metadata for a table is only retrieved the first time the connection to the table is made. To pick up new, changed, or deleted columns, you would need to delete and rebuild the metadata cache. Therefore, it is best to rely on the in-memory caching for cases where metadata changes often.
• When you are caching to a database: CacheMetadata can only be used with CacheLocation. If you are caching to another database with the CacheProvider and CacheConnection properties, use AutoCache to cache implicitly. Or, use CACHE Statements to cache explicitly.

This section provides a complete list of the Miscellaneous properties you can configure in the connection string for this provider.

By default, the provider will not automatically discover the metadata for a child table as its own distinct table. To enable this functionality, set DataModel to Relational .

Possible Values

Data Type

string

Default Value

"DOCUMENT"

Remarks

When setting DataModel to Relational, the discovery of child tables extends to root level elements and those found within top-level array elements.

By default, nested arrays are returned as strings of JSON. The FlattenArrays property can be used to flatten the elements of nested arrays into columns of their own. Set FlattenArrays to the number of elements you want to return from nested arrays.

Data Type

string

Default Value

""

Remarks

By default, nested arrays are returned as strings of JSON. The FlattenArrays property can be used to flatten the elements of nested arrays into columns of their own. This is only recommended for arrays that are expected to be short.

Set FlattenArrays to the number of elements you want to return from nested arrays. The specified elements are returned as columns. The zero-based index is concatenated to the column name. Other elements are ignored.

For example, you can return an arbitrary number of elements from an array of strings:

["FLOW-MATIC","LISP","COBOL"]

Setting FlattenArrays to -1 will flatten all the elements of nested arrays.

Set FlattenObjects to true to flatten object properties into columns of their own. Otherwise, objects nested in arrays are returned as strings of JSON.

Data Type

bool

Default Value

true

Remarks

Set FlattenObjects to true to flatten object properties into columns of their own. Otherwise, objects nested in arrays are returned as strings of JSON. To generate the column name, the Cloud concatenates the property name onto the object name with a dot.

For example, you can flatten the nested objects below at connection time:

[
     { "grade": "A", "score": 2 },
     { "grade": "A", "score": 6 },
     { "grade": "A", "score": 10 },
     { "grade": "A", "score": 9 },
     { "grade": "B", "score": 14 }
]

Indicates the user preference as to when schemas should be generated and saved.

Possible Values

Data Type

string

Default Value

"Never"

Remarks

GenerateSchemaFiles enables you to save the table definitions identified by Automatic Schema Discovery. This property outputs schemas to .rsd files in the path specified by Location.

Available settings are the following:

• Never: A schema file will never be generated.
• OnUse: A schema file will be generated the first time a table is referenced, provided the schema file for the table does not already exist.
• OnStart: A schema file will be generated at connection time for any tables that do not currently have a schema file.
• OnCreate: A schema file will be generated by when running a CREATE TABLE SQL query.

Generate Schemas with SQL

When you set GenerateSchemaFiles to OnUse, the Cloud generates schemas as you execute SELECT queries. Schemas are generated for each table referenced in the query.

When you set GenerateSchemaFiles to OnCreate, schemas are only generated when a CREATE TABLE query is executed.

Generate Schemas on Connection

Another way to use this property is to obtain schemas for every table in your database when you connect. To do so, set GenerateSchemaFiles to OnStart and connect.

Alternatives to Static Schemas

If your data structures are volatile, consider setting GenerateSchemaFiles to Never and using dynamic schemas. See Automatic Schema Discovery for more information about dynamic schemas.

Editing Schemas

Schema files have a simple format that makes them easy to modify. See Custom Schema Definitions for more information.

Limits the number of rows returned rows when no aggregation or group by is used in the query. This helps avoid performance issues at design time.

Data Type

int

Default Value

-1

Remarks

Limits the number of rows returned rows when no aggregation or group by is used in the query. This helps avoid performance issues at design time.

The server normally times out idle cursors after an inactivity period (10 minutes) to prevent excess memory use. Set this option to prevent that.

Data Type

bool

Default Value

false

Remarks

The server normally times out idle cursors after an inactivity period (10 minutes) to prevent excess memory use. Set this option to prevent that.

These hidden properties are used only in specific use cases.

Data Type

string

Default Value

""

Remarks

The properties listed below are available for specific use cases. Normal driver use cases and functionality should not require these properties.

Specify multiple properties in a semicolon-separated list.

Integration and Formatting

The maximum number of results to return per page from MongoDB.

Data Type

int

Default Value

4096

Remarks

The Pagesize property affects the maximum number of results to return per page from MongoDB. Setting a higher value may result in better performance at the cost of additional memory allocated per page consumed.

This property indicates whether or not to include pseudo columns as columns to the table.

Data Type

string

Default Value

""

Remarks

This setting is particularly helpful in Entity Framework, which does not allow you to set a value for a pseudo column unless it is a table column. The value of this connection setting is of the format "Table1=Column1, Table1=Column2, Table2=Column3". You can use the "*" character to include all tables and all columns; for example, "*=*".

This option passes the query to MongoDB as-is.

Data Type

bool

Default Value

false

Remarks

When set to 'True', the specified query will be passed to MongoDB as-is. Currently only these shell commands are supported:

• db.myCollection.find() returns all fields for all records in the collection.
• db.myCollection.find({ query }) returns all fields for all records in the collection matching the query.
• db.myCollection.find({ query }, { projection }) returns the fields in the projection, for all records matching the query.
• All of the above forms accept a .json() suffix. This returns a single column containing the matching documents as JSON instead of individual fields.

Note that you can use the EVAL stored procedure to execute other JavaScript functions.

You can use this property to enforce read-only access to MongoDB from the provider.

Data Type

bool

Default Value

false

Remarks

If this property is set to true, the Cloud will allow only SELECT queries. INSERT, UPDATE, DELETE, and stored procedure queries will cause an error to be thrown.

Set this to a strategy for reading from a replica set. Accepted values are primary, primaryPreferred, secondary, secondaryPreferred, and nearest.

Data Type

string

Default Value

"primary"

Remarks

This property enables you to execute queries to a member in a replica set other other than the primary member. Accepted values are the following:

• primary: All SELECT queries are executed against the primary server.
• primaryPreferred: If the primary server is not available, SELECT queries are executed to a secondary server.
• secondary: All SELECT queries are executed to the secondary servers.
• secondaryPreferred: SELECT queries are executed to a secondary server if one is available. Otherwise, the queries are executed to the primary server.
• nearest: SELECT queries are executed to the server with the least latency.

When to Use ReadPreference

When this property is set, query results may not reflect the latest changes if a write operation has not yet been replicated to a secondary machine. You can use ReadPreference to accomplish the following, with some risk that the Cloud will return stale data:

• Configure failover queries: If the primary server is unavailable, you can set this property to "primaryPreferred" to continue to execute queries online.
• Execute faster queries to geographically distributed replica sets: If your deployment uses multiple data centers, setting ReadPreference to "nearest" can result in faster queries, as the Cloud executes SELECT queries to whichever replica set member has the lowest latency.

When directing the Cloud to execute SELECT statements to a secondary server, SlaveOK must also be set. Otherwise, the Cloud will return an error response.

Use this property to target a replica set member or members that are associated with tags.

Data Type

string

Default Value

""

Remarks

To make use of ReadPreferenceTags you must configure ReadPreference to a value other than the primary value (the default value). The required format is a list of semicolon seperated tag sets where each tag set is a list of key value pairs separated by commas. For example:

• tag1:val1,tag2:val2;: Find members with both tag values. If none are found, find any eligible member.
• tag1:val1;tag2:val2;: Find members with the specified tag1, otherwise find members with the specified tag2. If none are found find any eligible member.
• tag1:val1: Find only members with the specified tag.
• ;: (semicolon only) Find any eligible member. If left empty, any eligible member is targeted.

The maximum number of rows to scan to look for the columns available in a table.

Data Type

int

Default Value

100

Remarks

The columns in a table must be determined by scanning table rows. This value determines the maximum number of rows that will be scanned.

Setting a high value may decrease performance. Setting a low value may prevent the data type from being determined properly, especially when there is null data.

Setting to a value of -1 causes the Cloud to scan an arbitrary number of rows until it reaches the final row.

The runtime key used for licensing.

Data Type

string

Default Value

""

Remarks

The RTK property may be used to license a build.

This property sets whether the provider is allowed to read from secondary (slave) servers.

Data Type

bool

Default Value

false

Remarks

This property sets whether the Cloud is allowed to read from secondary (slave) servers in a replica set. You can fine-tune how the Cloud queries secondary servers with ReadPreference.

The value in seconds until the timeout error is thrown, canceling the operation.

Data Type

int

Default Value

60

Remarks

If Timeout = 0, operations do not time out. The operations run until they complete successfully or until they encounter an error condition.

If Timeout expires and the operation is not yet complete, the Cloud throws an exception.

Comma-separated options for how the provider will scan the data to determine the fields and datatypes in each document collection.

Data Type

string

Default Value

"RowScan"

Remarks

Sets replacing or merging target document with updating fields is performed by executing update statement.

Possible Values

Data Type

string

Default Value

"Default"

Remarks

Sets replacing or merging target document with updating fields is performed by executing update statement. When the default value Default is used, the Cloud updates the target document by replacing the whole original document with new one. When the value is set to Merge, only the specific field in the target document will be updated.

For example, if you have a collection 'classySample' as below.

{
  "_id": "1",
  "message": {
    "component_items": [{"locked": true}],
    "id":1
  }
}

UPDATE [classySample] SET [message.component_items.0.locked] = false  WHERE [message.id] = 1

In the query above, the 'message' document will be replaced with new document constructed with SET clause, the collection after updating looks like

{
  "_id": "1",
  "message": {
    "component_items": [
      {
        "locked": false
      }
    ]
  }
}

But when using Merge, only the 'locked' field in 'component_items' will be updated, the collection becomes

{
    "_id": "1",
    "message": {
        "component_items": [
            {
                "locked": false
            }
        ],
        "id": 1
    }
}

Execute MongoDB queries using db.collection.find().

Data Type

string

Default Value

"False"

Remarks

Amazon DocumentDB doesn't support the legacy OP_QUERY interface, so this must be set to True to query DocumentDB clusters with db.collection.find() instead.

A filepath pointing to the JSON configuration file containing your custom views.

Data Type

string

Default Value

""

Remarks

User Defined Views are defined in a JSON-formatted configuration file called UserDefinedViews.json. The Cloud automatically detects the views specified in this file.

You can also have multiple view definitions and control them using the UserDefinedViews connection property. When you use this property, only the specified views are seen by the Cloud.

This User Defined View configuration file is formatted as follows:

• Each root element defines the name of a view.
• Each root element contains a child element, called query, which contains the custom SQL query for the view.

For example:

{
	"MyView": {
		"query": "SELECT * FROM [CData].[Sample].Customers WHERE MyColumn = 'value'"
	},
	"MyView2": {
		"query": "SELECT * FROM MyTable WHERE Id IN (1,2,3)"
	}
}

"UserDefinedViews", "C:\\Users\\yourusername\\Desktop\\tmp\\UserDefinedViews.json"

Requests acknowledgment that the write operation has propagated to the specified number of mongod instances.

Data Type

string

Default Value

"0"

Remarks

Requests acknowledgment that the write operation has propagated to the specified number of mongod instances.

Requires acknowledgment that the mongod instances, as specified in the WriteConcern property, have written to the on-disk journal.

Data Type

bool

Default Value

true

Remarks

It requests acknowledgment that the mongod instances, as specified in the WriteConcern property, have written to the on-disk journal.

This option specifies a time limit, in milliseconds, for the write concern.

Data Type

string

Default Value

"0"

Remarks

This option specifies a time limit, in milliseconds, for the write concern.

Sets whether the object type for inserted or updated objects is determined from the existing column metadata or the input value type.

Possible Values

Data Type

string

Default Value

"Metadata"

Remarks

Sets whether the object type for inserted or updated objects is determined from the existing column metadata or the input value type. When the default value Metadata is used, the Cloud uses the data type as determined by the TypeDetectionScheme for objects pushed to MongoDB. When the value is set to RawValue, the type of the object in the INSERT determines what type is used for MongoDB.

For example, if you have a field 'c1' in MongoDB defined as String type, the metadata returns the column as String as well. In the following query, the resulting field in MongoDB is therefore defined as String when using WriteScheme=Metadata. But when using RawValue, the inserting field type is Date instead since the FROM_UNIXTIME() function returns an actual Date object:

INSERT into Table1 (c1) VALUES(FROM_UNIXTIME(1636910867039, 0))

Inserting an empty array

INSERTINTO t1 ("c1")VALUES(())

This returns an empty array:

"c1":[]