Automatically caching data is useful when you do not want to rebuild the cache for each query. When you query data for the first time, the provider automatically initializes and builds a cache in the background. When AutoCache = true, the provider uses the cache for subsequent query executions, resulting in faster response times.

Configuring Automatic Caching

Caching the Customers Table

The following example caches the Customers table in the file specified by the CacheLocation property of the connection string.

C#

String connectionString = "Cache Location=C:\\cache.db;AutoCache=true;Server=127.0.0.1;";
using (SparkSQLConnection connection = new SparkSQLConnection(connectionString)) {
  SparkSQLCommand cmd = new SparkSQLCommand("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", connection);
  SparkSQLDataReader rdr = cmd.ExecuteReader();
  while (rdr.Read()) {
    Console.WriteLine("Read and cached the row with _id " + rdr["_id"]);
  }
}

VB.NET

Dim connectionString As [String] = "Cache Location=C:\\cache.db;AutoCache=true;Server=127.0.0.1;"
Using connection As New SparkSQLConnection(connectionString)
  Dim cmd As New SparkSQLCommand("SELECT City, CompanyName FROM Customers WHERE Country = 'US'", connection)
  Dim rdr As SparkSQLDataReader = cmd.ExecuteReader()
  While rdr.Read()
	  Console.WriteLine("Read and cached the row with _id " + rdr("_id"))
  End While
End Using

Common Use Case

A common use for automatically caching data is to improve driver performance when making repeated requests to a live data source, such as building a report or creating a visualization. With auto caching enabled, repeated requests to the same data may be executed in a short period of time, but within an allowable tolerance (CacheTolerance) of what is considered "live" data.