Using AlwaysOn SQL service

AlwaysOn SQL is a high availability service that responds to SQL queries from JDBC and ODBC applications.

cassandra.yaml

The location of the cassandra.yaml file depends on the type of installation:

Package installations	/etc/dse/cassandra/cassandra.yaml
Tarball installations	`installation_location`/resources/cassandra/conf/cassandra.yaml

spark-alwayson-sql.conf

For use with AlwaysOn SQL, the default location of the spark-alwayson-sql.conf file is:

Package installations	/etc/dse/spark/spark-alwayson-sql.conf
Tarball installations	`installation_location`/resources/spark/conf/spark-alwayson-sql.conf

dse.yaml

The location of the dse.yaml file depends on the type of installation:

Package installations	/etc/dse/dse.yaml
Tarball installations	`installation_location`/resources/dse/conf/dse.yaml

AlwaysOn SQL is a high availability service that responds to SQL queries from JDBC and ODBC applications. By default, AlwaysOn SQL is disabled. It is built on top of the Spark SQL Thriftserver, but provides failover and caching between instances so there is no single point of failure. AlwaysOn SQL provides enhanced security, leveraging the same user management as the rest of DSE, executing queries to the underlying database as the user authenticated to AlwaysOn SQL.

In order to run AlwaysOn SQL, you must have:

A running datacenter with DSE Analytics nodes enabled.
Enabled AlwaysOn SQL on every Analytics node in the datacenter.
Modified the replication factor for all Analytics nodes, if necessary.
Set the native_transport_address in cassandra.yaml to an IP address accessible by the AlwaysOn SQL clients. This address depends on your network topology and deployment scenario.
Configured AlwaysOn SQL for security, if authentication is enabled.

Lifecycle Manager allows you to enable and configure AlwaysOn SQL in managed clusters.

When AlwaysOn SQL is enabled within an Analytics datacenter, all nodes within the datacenter must have AlwaysOn SQL enabled. Use dsetool ring to find which nodes in the datacenter are Analytics nodes.

Note: AlwaysOn SQL is not supported when using DSE Multi-Instance or other deployments with multiple DSE instances on the same server.

The dse client-tool alwayson-sql command controls the server. The command works on the local datacenter unless you specify the datacenter with the --dc option:

dse client-tool alwayson-sql --dc datacenter_name command

Enabling AlwaysOn SQL

Set enabled to true and uncomment the AlwaysOn SQL options in dse.yaml .

Configuring AlwaysOn SQL

The alwayson_sql_options section in dse.yaml, described in detail at AlwaysOn SQL options, has options for setting the ports, timeout values, log location, and other Spark or Hive configuration settings. Additional configuration options are located in spark-alwayson-sql.conf.

AlwaysOn SQL binds to the native_transport_address in cassandra.yaml.

If you have changed some configuration settings in dse.yaml while AlwaysOn SQL is running, you can have the server pick up the new configuration by entering:

dse client-tool alwayson-sql reconfig

The following settings can be changed using reconfig:

reserve_port_wait_time_ms
alwayson_sql_status_check_wait_time_ms
log_dsefs_dir
runner_max_errors

Changing other options requires a restart, except for the enabled option. Enabling or disabling AlwaysOn SQL requires restarting DSE.

The spark-alwayson-sql.conf file contains Spark and Hive settings as properties. When AlwaysOn SQL is started, spark-alwayson-sql.conf is scanned for Spark properties, similar to other Spark applications started with dse spark-submit. Properties that begin with spark.hive are submitted as properties using --hiveconf, removing the spark. prefix.

For example, if spark-alwayson-sql.conf has the following setting:

spark.hive.server2.table.type.mapping CLASSIC

That setting will be converted to --hiveconf hive.server2.table.type.mapping=CLASSIC when AlwaysOn SQL is started.

Configuring AlwaysOnSQL in a DSE Analytics Solo datacenter

If AlwaysOn SQL is used in a DSE Analytics Solo datacenter, modify spark-alwayson-sql.conf to configure Spark with the DSE Analytics Solo datacenters. In the following example, the transactional datacenter name is dc0 and the DSE Analytics Solo datacenter is dc1.

Under spark.master set the Spark URI to the connect to the DSE Analytics Solo datacenter.

spark.master=dse://?connection.local_dc=dc1

Add the spark.cassandra.connection.local_dc property to spark-alwayson-sql.conf and set it to the name of the transactional datacenter.

spark.cassandra.connection.local_dc=dc0

Starting and stopping AlwaysOn SQL

If you have enabled AlwaysOn SQL, it will start when the cluster is started. If AlwaysOn SQL is enabled and DSE is restarted, AlwaysOn SQL will be started regardless of the previous state of AlwaysOn SQL. You only need to explicitly start the server if it has been stopped, for example for a configuration change.

To start AlwaysOn SQL service:

dse client-tool alwayson-sql start

To start the server on a specific datacenter, specify the datacenter name with the --dc option:

dse client-tool alwayson-sql --dc dc-west start

To completely stop AlwaysOn SQL service:

dse client-tool alwayson-sql stop

The server must be manually started after issuing a stop command.

To restart a running server:

dse client-tool alwayson-sql restart

Checking the status of AlwaysOn SQL

To find the status of AlwaysOn SQL issue a status command using dse-client-tool.

dse client-tool alwayson-sql status

You can also view the status in a web browser by going to http://node name or IP address:AlwaysOn SQL web UI port. By default, the port is 9077. For example, if 10.10.10.1 is the IP address of an Analytics node with AlwaysOn SQL enabled, navigate to http://10.10.10.1:9077.

The returned status is one of:

RUNNING: the server is running and ready to accept client requests.
STOPPED_AUTO_RESTART: the server is being started but is not yet ready to accept client requests.
STOPPED_MANUAL_RESTART: the server was stopped with either a stop or restart command. If the server was issued a restart command, the status will be changed to STOPPED_AUTO_RESTART as the server starts again.
STARTING: the server is actively starting up but is not yet ready to accept client requests.

Caching tables within Spark SQL queries

To increase performance, you can specify tables to be cached into RAM using the CACHE TABLE directive. Permanent cached tables will be recached on server restart.

You can cache an existing table by issuing a CACHE TABLE Spark SQL command through a client:

CACHE TABLE keyspace_name.table_name;

CACHE TABLE keyspace_name.table_name AS select statement;

The temporary cache table is only valid for the session in which it was created, and will not be recreated on server restart.

Create a permanent cache table using the CREATE CACHE TABLE directive and a SELECT query:

CREATE CACHE TABLE keyspace_name.table_name AS select_statement;

The table cache can be destroyed using the UNCACHE TABLE and CLEAR CACHE directives.

UNCACHE TABLE keyspace_name.table_name;

The CLEAR CACHE directive removes the table cache.

CLEAR CACHE;

Issuing DROP TABLE will remove all metadata including the table cache.