Starting a Spark cluster

This section contains expanded instructions for starting a Spark cluster. Basic start operations are described in the Quick start section.

Auto-scaler

To save resources of the computational pool if the load is low, a special auto-scaler mode can be turned on in Spark, which proportionally decreases the resources used.

How it works

The operation YTsaurus has an update_operation_parameters method that enables the operation parameters to be changed. The number of jobs in the operation can be changed via the user_slots parameter. When the parameter is changed, the scheduler stops some of the jobs, or launches new ones (within the limit specified at the start of the operation). Since the scheduler believes that all jobs in the operation are the same, this scaling method, performed in the regular mode in Spark, could lead to loss of the master or the history server, as well as of workers that Spark job drivers are being performed for. To prevent disruptions to the Spark cluster's operation, it is started not as a single YTsaurus operation, but as several. This way, one operation is allocated to the dynamically changing set of workers, and can scale within the limits configured at the start. In one or two other operations, a master and history-server (one operation), or a driver (when drivers are launched on the cluster, two operations) are performed.

Starting a cluster with an auto-scaler

Additional parameters are used for the spark-launch-yt launch script, or similar parameters of the SPYT client library.

• The autoscaler-period <period> is the frequency of auto-scaler launches, and (potentially) of changes in the operation settings. The period is programmed in <length><unit of measurement [d|h|min|s|ms|µs|ns]>.
• enable-multi-operation-mode turns on Spark start mode in multiple YTsaurus operations.
• enable-dedicated-driver-operation-mode launches workers for drivers in a separate YTsaurus operation.
• driver-num <number of workers> allocates a certain number of workers for the driver.
• autoscaler-max-free-workers is the maximum value of free workers (all superfluous workers will be stopped).
• autoscaler-slot-increment-step is the increment in which the number of workers is increased when the cluster is automatically expanded.

Example:

$ spark-launch-yt
--proxy <cluster_name>
--autoscaler-period 1s
--enable-multi-operation-mode
--discovery-path //discovery/path

Updating a cluster

To update a Spark cluster, the following actions must be performed:

1. Stop the operation with the current cluster in YTsaurus. You can find a link to the operation using spark-discovery-yt. You can also use the --abort-existing flag of the spark-launch-yt command. In this case, the current cluster will be stopped before the new one starts.
2. Start a cluster using spark-launch-yt. The desired version can be specified in the spark-cluster-version argument. If no version is specified, the last version will be started.

spark-launch-yt parameters

To start an internal Spark cluster, use the spark-launch-yt command and pass a number of parameters to it. They are described in the table below: