Parallel Inserts

If you have a queue of inserts, one way to process them in your client is to send them one by one, waiting for the response from previous insert before sending the next one.

With a latency of 5ms between the server and client, even if inserts happened instantaneously, this client could only ever do 200 inserts per second.

If you’re handling a lot of inserts, this sort of setup can very quickly become a performance bottleneck.

The solution to this is to send multiple inserts concurrently. That is, send off every insert request as soon as you need to and do not wait for a response before sending another insert.


Before trying to parallelize your queries, you should evaluate whether Bulk Operations are a good fit. In many cases, you will see even better performance from bulk opperations.

Table of Contents


Suppose we have a stream of data we want to persist into CrateDB. You can parallelize this in Java using a CompletableFuture object, like so:

IntStream.iterate(0, i -> i + 2)
    .forEach(i -> {
        CompletableFuture<Integer> insertFuture =
            CompletableFuture.supplyAsync(() -> {
                try {
                    PreparedStatement stmt =
                      connection.prepareStatement("INSERT INTO my_table VALUES (?)");
                    stmt.setInt(1, i);
                    return stmt.executeUpdate();
                } catch (SQLException e) {
                    throw new RuntimeException(e);

        insertFuture.whenComplete((Integer result, Throwable failure) -> {
            if (failure == null) {
                // use row count
            } else {
                // handle insert failure

Inserts will be executed asynchronously by the commmonPool object.

You can provide your own Executor using any object with the appropriate supplyAsync signature.


Follow the basic inserts performance testing procedure.

To test parallel inserts, you should:

  1. Configure the setup you would like to test
  2. Run a number of different tests against that setup, using different --concurrency settings
  3. Evaluate your throughput results (perhaps by plotting your results on a graph so that you can see the response curve)

Try out different setups and re-run the test.

At the end of this process, you will have a better understanding of the throughput of your cluster with different setups and under different loads.