Request throttling

Throttling allows you to limit how many requests a session can execute concurrently. This is useful if you have multiple applications connecting to the same Cassandra cluster, and want to enforce some kind of SLA to ensure fair resource allocation.

The request throttler tracks the level of utilization of the session, and lets requests proceed as long as it is under a predefined threshold. When that threshold is exceeded, requests are enqueued and will be allowed to proceed when utilization goes back to normal.

From a user’s perspective, this process is mostly transparent: any time spent in the queue is included in the session.execute() or session.executeAsync() call. Similarly, the request timeout encompasses throttling: it starts ticking before the request is passed to the throttler; in other words, a request may time out while it is still in the throttler’s queue, before the driver has even tried to send it to a node.

The only visible effect is that a request may fail with a RequestThrottlingException, if the throttler has determined that it can neither allow the request to proceed now, nor enqueue it; this indicates that your session is overloaded. How you react to that is specific to your application; typically, you could display an error asking the end user to retry later.

Note that the following requests are also affected by throttling:

  • preparing a statement (either directly, or indirectly when the driver reprepares on other nodes, or when a node comes back up – see how the driver prepares);
  • fetching the next page of a result set (which happens in the background when you iterate the synchronous variant ResultSet).
  • fetching a query trace.


Request throttling is parameterized in the configuration under advanced.throttler. There are various implementations, detailed in the following sections:

Pass through

datastax-java-driver {
  advanced.throttler {
    class = PassThroughRequestThrottler

This is a no-op implementation: requests are simply allowed to proceed all the time, never enqueued.

Note that you will still hit a limit if all your connections run out of stream ids. In that case, requests will fail with an AllNodesFailedException, with the getErrors() method returning a BusyConnectionException for each node.


datastax-java-driver {
  advanced.throttler {
    class = ConcurrencyLimitingRequestThrottler

    # Note: the values below are for illustration purposes only, not prescriptive
    max-concurrent-requests = 10000
    max-queue-size = 100000

This implementation limits the number of requests that are allowed to execute simultaneously. Additional requests get enqueued up to the configured limit. Every time an active request completes (either by succeeding, failing or timing out), the oldest enqueued request is allowed to proceed.

Make sure you pick a threshold that is consistent with your pooling settings; the driver should never run out of stream ids before reaching the maximum concurrency, otherwise requests will fail with BusyConnectionException instead of being throttled. The total number of stream ids is a function of the number of connected nodes and the connection.pool.*.size and connection.max-requests-per-connection configuration options. Keep in mind that aggressive speculative executions and timeout options can inflate stream id consumption, so keep a safety margin. One good way to get this right is to track the pool.available-streams metric on every node, and make sure it never reaches 0.


datastax-java-driver {
  advanced.throttler {
    class = RateLimitingRequestThrottler

    # Note: the values below are for illustration purposes only, not prescriptive
    max-requests-per-second = 5000
    max-queue-size = 50000
    drain-interval = 1 millisecond

This implementation tracks the rate at which requests start, and enqueues when it exceeds the configured threshold.

With this approach, we can’t dequeue when requests complete, because having less active requests does not necessarily mean that the rate is back to normal. So instead the throttler re-checks the rate periodically and dequeues when possible, this is controlled by the drain-interval option. Picking the right interval is a matter of balance: too low might consume too many resources and only dequeue a few requests at a time, but too high will delay your requests too much; start with a few milliseconds and use the cql-requests metric to check the impact on your latencies.

Like with the concurrency-based throttler, you should make sure that your target rate is in line with the pooling options; see the recommendations in the previous section.


Enable the following metrics to monitor how the throttler is performing:

datastax-java-driver {
  advanced.metrics.session.enabled = [
    # How long requests are being throttled (exposed as a Timer).
    # This is the time between the start of the session.execute() call, and the moment when the
    # throttler allows the request to proceed.

    # The size of the throttling queue (exposed as a Gauge<Integer>).
    # This is the number of requests that the throttler is currently delaying in order to
    # preserve its SLA. This metric only works with the built-in concurrency- and rate-based
    # throttlers; in other cases, it will always be 0.

    # The number of times a request was rejected with a RequestThrottlingException (exposed as a
    # Counter)

If you enable throttling.delay, make sure to also check the associated extra options to correctly size the underlying histograms (metrics.session.throttling.delay.*).