Speculative query execution

Sometimes a server node might be experiencing difficulties (for example, long GC pause) and take longer than usual to reply. Queries sent to that node experience higher latencies than expected.

One thing we can do to improve that is preemptively 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):

Text Diagram

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:

Text Diagram

Note that “cancelling” in this context simply means marking the operation to discard the response when it later arrives.

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

Query idempotence

One important aspect to consider is whether queries are idempotent, (that is, whether they can be applied multiple times without changing the result beyond the initial application). If a query is not idempotent, the driver never schedules speculative executions for it, because there is no way to guarantee that only one node will apply the mutation.

Examples of queries that are not idempotent are:

  • counter operations
  • prepending or appending to a list column
  • using non-idempotent CQL functions, like now() or uuid()

In the driver, this is determined by isIdempotent flag in the QueryOptions. Because the driver does not parse query strings, in most cases it has no information about what the query actually does. Therefore, for all other types of statements, it defaults to false. You must set it manually with one of the mechanisms described below.

You can override the value for each execution:

const query = 'SELECT * FROM users WHERE key = ?';
client.execute(query, [ 'usr1' ], { prepare: true, isIdempotent: true }); 

Additionally, if you know for a fact that your application does not use any of the non-idempotent CQL queries listed above, you can change the default cluster-wide:

// Make all statements idempotent by default:
const client = new Client({
  queryOptions: { 
    isIdempotent: true

Enabling speculative execution

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

The driver provides a ConstantSpeculativeExecutionPolicy that schedules a given number of speculative executions, separated by a fixed delay, the policy is exported under the <root>.policies.speculativeExecution module.

This simple policy uses a constant threshold:

const { Client, policies } = require('cassandra-driver');
const ConstantSpeculativeExecutionPolicy = policies.speculativeExecution.ConstantSpeculativeExecutionPolicy;

const client = new Client({
  policies: {
    speculativeExecution: new ConstantSpeculativeExecutionPolicy(
      200, // delay before a new execution is launched
      2) // maximum amount of additional executions

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

  • start the initial execution at t0
  • if no response has been received at t0 + 200 milliseconds, start a speculative execution on another node
  • if no response has been received at t0 + 400 milliseconds, start another speculative execution on a third node

As with the rest of policies in the driver, you can provide your own implementation by extending the SpeculativeExecutionPolicy prototype.

How speculative executions affect retries

Regardless of speculative executions, the driver has a retry mechanism:

  • on an internal error, it will try the next host
  • if the consistency level cannot be reached (for example, unavailable error or read or write timeout), it delegates the decision to the RetryPolicy, which might trigger a retry on the same host

Turning speculative executions on does not change this behavior. Each parallel execution trigger retries independently:

Text Diagram

The only impact is that all executions of the same query always share the same query plan, so each host is 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 does not necessarily improve.

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.

This problem is more likely to happen with old server versions (Apache Cassandra version 2.0 or below and DSE 4.6 or below) which only support version 1 and 2 of the native protocol where each TCP connection only has 128 available stream ids. With modern server versions, there are 32K stream ids per connection, so higher cancellation rates can be sustained.

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 us 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!

Using query timestamps, which are enabled by default, prevents this issue to appear as each request will have a client-level timestamp which will define the order to apply the mutations.