Speculative query execution

Sometimes a Cassandra node might be experiencing difficulties (ex: long GC pause) and take longer than usual to reply. Queries sent to that node will experience bad latency.

One thing we can do to improve that is pre-emptively start a second execution of the query against another node, before the first node has replied or errored out. If that second node replies faster, we can send the response back to the client (we also cancel the first query):

Or the first node could reply just after the second execution was started. In this case, we cancel the second execution. In other words, whichever node replies faster “wins” and completes the client query:

Speculative executions are disabled by default. The following sections cover the practical details and how to enable them.

Query idempotence

If a query is not idempotent, the driver will never schedule speculative executions for it, because there is no way to guarantee that only one node will apply the mutation.

Enabling speculative executions

Speculative executions are controlled by an instance of SpeculativeExecutionPolicy provided when initializing the Cluster. This policy defines the threshold after which a new speculative execution will be triggered.

Two implementations are provided with the driver:

ConstantSpeculativeExecutionPolicy

This simple policy uses a constant threshold:

Cluster cluster = Cluster.builder()
    .addContactPoint("127.0.0.1")
    .withSpeculativeExecutionPolicy(
        new ConstantSpeculativeExecutionPolicy(
            500, // delay before a new execution is launched
            2    // maximum number of executions
        ))
    .build();

Given the above configuration, an idempotent query would be handled this way:

• start the initial execution at t0;
• if no response has been received at t0 + 500 milliseconds, start a speculative execution on another node;
• if no response has been received at t0 + 1000 milliseconds, start another speculative execution on a third node.

PercentileSpeculativeExecutionPolicy

This policy sets the threshold at a given latency percentile for the current host, based on recent statistics.

First and foremost, make sure that the HdrHistogram library (used under the hood to collect latencies) is in your classpath. It’s defined as an optional dependency in the driver’s POM, so you’ll need to explicitly depend on it:

<dependency>
  <groupId>org.hdrhistogram</groupId>
  <artifactId>HdrHistogram</artifactId>
  <version>2.1.4</version>
</dependency>

Then create a PercentileTracker that will collect latency statistics for your Cluster. Two implementations are provided with the driver:

• PerHostPercentileTracker: maintains one histogram per host. A given host is only compared to itself, so its latencies will rank in the higher percentiles only if it’s slower than its usual performance;
• ClusterWidePercentileTracker: maintains a single histogram for the whole cluster. Hosts are compared against each other, so a host that is consistently slower than the rest of the cluster will get bad rankings.

We recommend trying ClusterWidePercentileTracker first, as it has produced the best results in our tests. You may also extend PercentileTracker with your own implementation.

// There are more options than shown here, please refer to the API docs
// for more information
PercentileTracker tracker = ClusterWidePercentileTracker
    .builder(15000)
    .build();

Next, create an instance of the policy with the tracker, and pass it to your cluster:

PercentileSpeculativeExecutionPolicy policy =
    new PercentileSpeculativeExecutionPolicy(
        tracker,
        99.0,     // percentile
        2);       // maximum number of executions

Cluster cluster = Cluster.builder()
    .addContactPoint("127.0.0.1")
    .withSpeculativeExecutionPolicy(policy)
    .build();

Note that PercentileTracker may also be used with a slow query logger (see the Logging section). In that case, you would create a single tracker object and share it with both components.

Using your own

As with all policies, you are free to provide your own by implementing SpeculativeExecutionPolicy.

How speculative executions affect retries

Turning speculative executions on doesn’t change the driver’s retry behavior. Each parallel execution will trigger retries independently:

The only impact is that all executions of the same query always share the same query plan, so each host will be used by at most one execution.

Tuning and practical details

The goal of speculative executions is to improve overall latency (the time between execute(query) and complete in the diagrams above) at high percentiles. On the flipside, they cause the driver to send more individual requests, so throughput will not necessarily improve.

You can monitor how many speculative executions were triggered with the speculative-executions metric (exposed in the Java API as cluster.getMetrics().getErrors().getSpeculativeExecutions()). It should only be a few percents of the total number of requests (cluster.getMetrics().getRequestsTimer().getCount()).

Stream id exhaustion

One side-effect of speculative executions is that many requests are cancelled, which can lead to a phenomenon called stream id exhaustion: each TCP connection can handle multiple simultaneous requests, identified by a unique number called stream id. When a request gets cancelled, we can’t reuse its stream id immediately because we might still receive a response from the server later. If this happens often, the number of available stream ids diminishes over time, and when it goes below a given threshold we close the connection and create a new one. If requests are often cancelled, so will see connections being recycled at a high rate.

One way to detect this is to monitor open connections per host (Session.getState().getOpenConnections(host)) against TCP connections at the OS level. If open connections stay constant but you see many TCP connections in closing states, you might be running into this issue. Try raising the speculative execution threshold.

This problem is more likely to happen with version 2 of the native protocol, because each TCP connection only has 128 stream ids. With version 3 (driver 2.1.2 or above with Cassandra 2.1 or above), there are 32K stream ids per connection, so higher cancellation rates can be sustained. If you’re unsure of which native protocol version you’re using, you can check with cluster.getConfiguration().getProtocolOptions().getProtocolVersion().

Request ordering and client timestamps

Another issue that might arise is that you get unintuitive results because of request ordering. Suppose you run the following query with speculative executions enabled:

insert into my_table (k, v) values (1, 1);

The first execution is a bit too slow, so a second execution gets triggered. Finally, the first execution completes, so the client code gets back an acknowledgement, and the second execution is cancelled. However, cancelling only means that the driver stops waiting for the server’s response, the request could still be “on the wire”; let’s assume that this is the case.

Now you run the following query, which completes successfully:

delete from my_table where k = 1;

But now the second execution of the first query finally reaches its target node, which applies the mutation. The row that you’ve just deleted is back!

The workaround is to use a timestamp with your queries:

insert into my_table (k, v) values (1, 1) USING TIMESTAMP 1432764000;

If you’re using native protocol v3, you can also enable client-side timestamps to have this done automatically.