Optimising node performance

Node performance optimisation can be achieved by adjusting node configuration, node memory, node thread count.

The main parameters that can be tweaked for a Corda Enterprise node are

  • The number of flow threads (the number of flows that can be live and active in the state machine at the same time). The default value for this is twice the number of processor cores available on the machine, capped at 30.
  • The number of RPC threads (the number of calls the RPC server can handle in parallel, enqueuing requests to the state machine). The default for this is the number of processor cores available on the machine
  • The amount of heap space the node process can allocate. The default for this is 512 megabytes.

For a machine with n cores, this will create up to 3n* Corda threads. On top of that, the messaging system (Artemis and Netty) will create their own messaging handling thread infrastructure. On a server machine with many processor cores, this can lead to over a 100 threads sharing 512 megabyte of memory - this leaves the threads fighting for resources, and memory contention and very poor performance will be the result.

In Corda Enterprise, these properties can be controlled via the node configuration. R3 recommends to keep a diary of changes during the tweaking of any parameters, or ideally have the node configuration under version control so it is easy to go back and check settings for previous results.

The first tweak should be to give the node more memory - the instructions how to deploy a node recommend at least 2GB of memory. Performance tests at R3 typically use 8GB of memory for one node. This depends on the available memory and how many nodes (and other processes) are run on the same machine. There are various ways to set the heap memory of the node documented at Setting JVM arguments. The recommended approach for performance optimisation work is to use the JVM argument section in the node config file as this captures the memory setting along with any other settings.

Be careful with the total amount of memory allocated to processes - if the total memory allocated to all processes on one machine exceeds the physical RAM available in the machine, memory will be swapped out to the file system (if this is enabled) or memory allocation will fail. Corda node processes do not react kindly to either of those events. In case of a memory allocation failure, the node will stop and needs to be restarted. If the memory of the node process gets swapped out, expected timings and guarantees within the process can be broken. This can lead to severe performance degradation, and flows and apps that rely on timings might fail.

In general, more memory is better for the node, so it might be a good idea to start with as much memory as can be made available without running into the issues mentioned above, and then proceed with tweaking other parameters. Once the node is tuned, it might be worthwhile to run a few tests checking whether the amount of memory can be reduced without affecting performance.

Especially on large server machines, the default number of flow threads might be on the upper limit of what is sensible. In order to find the optimal number, it is necessary to tweak that number via the configuration, restart the node(s), and rerun a test plan to see how the numbers have changed. In order to keep the tests reproducible, it might be a good idea to wipe the database between tests so index sizes and query times do not skew the test results for later runs (see Resetting a Node).

Flow and RPC threads can be set explicitly using the tuning section of the enterprise configuration. Add the following section to your node configuration file:

enterpriseConfiguration = {
    tuning = {
        rpcThreadPoolSize = 4
        flowThreadPoolSize = 8

The recommended approach is to start with a low number of flow threads (e.g. 1 per gigabyte of heap memory), and increase the number of threads over a number of runs. In tests at R3, it seems that giving a node twice the number of flow threads than RPC threads seemed a sensible number, but that might depend on the hardware and the use case, so it is worthwhile to experiment with this ratio.

The node needs to write log files to disk, and has an Artemis spool directory that is used for the durable queues on the hard disk, so disk I/O for the node’s working directory has an impact on the node performance. For optimal performance, this should be on a fast, local disk.

For the Artemis spool directory, this leads to an inherent contradiction, a trade-off for which needs to be carefully considered and tested for a production installation: the durable queue spool directory should be as fast as possible in order not to become a bottleneck, on the other hand this data must not get lost or the durable promise of the queue will be broken, so it should ideally be on a redundant storage medium.

The node has a high level of interaction with its database, so the performance of the database has a large impact on the node performance.

The H2 database that is used by Corda by default is very handy for development, but it cannot handle the throughput that a serious performance test will generate on any sensible hardware. It has internal locks that will throttle the throughput of the node. To test actual performance of a Corda system, it is required to use an enterprise level database.

The database should be running on a separate server. Corda has some rather unusual requirements for the database: as the node writes its checkpoints to the database, but only ever reads them when a flow needs to be restarted, the amount of data written to the database can vastly exceed the amount of data read and index look-ups performed. Checkpoints are usually written once and removed once the flow finishes.

Therefore, a standard, read-optimised database as is e.g. on offer from cloud providers does not suit the performance requirements of Corda very well. R3 recommends to run a dedicated database on a server that has fast disks, so the writing of checkpoints does not slow the processing down.

Depending on the write performance of the database, it might be useful to have a separate database server for each node rather than having a schema per node in the same database server.

For any flow that only works within one node (e.g. Cash Issuance), the above should allow to tweak the node to be performant. Any flows that involve connections to other nodes (e.g. to the recipient of a payment or a notary) might also be bottlenecked on the performance of their peers, so they might need to be tweaked as well.

How much memory and how many threads are required on a peer node depends on the app being tested. When using the CashIssueAndPayment flow from the performance CorDapp, the receiving node typically only needs half the number of threads/memory compared to the issuing node to keep up with processing, but this might also depend on the hardware. Keeping one node configuration constant and modifying a peer configuration is a valid test that needs to be undertaken.

Any flows that require notarisations might be limited by the throughput of the notary.

Corda uses TLS to encrypt the peer to peer connections between nodes. It can be shown that the maximal throughput that is achievable with the JVM TLS layer can limit the node throughput for flows that need to send sufficient amount of data between peers. This is e.g. the case for the CashIssueAndPayment flow in the performance test CorDapp.

Corda nodes can optionally use boringSsl for TLS connections - this is an OpenSsl based native SSL/TLS library maintained by Google that allows much higher peer to peer throughput. Its use can be enabled by adding the following to the node configuration file:


The throughput of the system also varies with the load that is created from the client side - if the incoming request rate is too low, the system will not reach maximum throughput, if it is too high, resource contention might actually lower the throughput as well. Varying the load to identify the number of connections at which the node gets saturated and at which point throughput might start to suffer is not only important to get optimal performance number, but this also needs to be considered when specifying a production system that needs to be able to handle a certain load.

The Number of Threads setting on a Thread Group in the test plan controls how many threads JMeter will run in parallel, each starting a run and then waiting for the result. If you look at the NightlyBenchmark example test plan, you’ll notice that the same test gets repeated with different numbers of threads, thus creating a result that reports throughput as a function of number of clients.

Increasing the number of threads too high might lead to contention on the JMeter server (e.g. on the RPC client), therefore it is also possible to run requests from several JMeter servers. In this case, it is important that the target host in the testplan is an actual machine name or IP address, not localhost, as the JMeter servers might run on different machines. Each JMeter server will run the specified number of threads, so five servers with 200 Threads each would lead to 1000 runs in parallel.

After optimising the node using a specific test plan, do keep in mind that this might have optimised the node for this specific load that gets generated by JMeter. While this is probably a good starting point to configure nodes for real world production uses, it is in no way guaranteed that the configuration is suitable.

If monitoring a node reveals that it does not perform as expected, further tweaking might be required, or the creation of a test plan that matches the usage pattern observed with real life use and with the CorDapps that get used.

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