Scalability of Orthanc

Overview

One of the most common question about Orthanc is: “How many DICOM instances can be stored by Orthanc?”

The source code of Orthanc imposes no such hard limit by itself.

At the time of writing, we know that Orthanc is being used in production in hospitals with more than 15TB of data, 125,000 studies and around 50 millions of instances (please get in touch with us if you can share other testimonials). Other users have even reported more than 28TB of data. Here are links to some testimonials that were published on the Orthanc Users discussion forum discussion group: 1, 2, 3, 4, 5, 6…

The stress is actually put on the underlying database engine, and on the storage area (check out How does Orthanc store its database?). As explained in the troubleshooting section, the built-in SQLite database engine should be replaced by an enterprise-ready database engine once Orthanc must store several hundreds of thousands of DICOM instances (check out the PostgreSQL plugins and MySQL/MariaDB plugins). It is also true that the performance of Orthanc in the presence of large databases has continuously improved over time, especially when it comes to the speed of DICOM C-FIND.

Controlling memory usage

The absence of memory leaks in Orthanc is verified thanks to valgrind.

On GNU/Linux systems, you might however observe a large memory consumption in the “resident set size” (VmRSS) of the application, notably if you upload multiple large DICOM files using the REST API.

This large memory consumption comes from the fact that the embedded HTTP server is heavily multi-threaded, and that many so-called memory arenas are created by the glibc standard library (up to one per thread). As a consequence, if each one of the 50 threads in the HTTP server of Orthanc (default value of the HttpThreadsCount option) allocates at some point, say, 50MB, the total memory usage reported as “VmRSS” can grow up to 50 threads x 50MB = 2.5GB, even if the Orthanc threads properly free all the buffers.

A possible solution to reducing this memory usage is to ask glibc to limit the number of “memory arenas” that are used by the Orthanc process. On GNU/Linux, this can be controlled by setting the environment variable MALLOC_ARENA_MAX. For instance, the following bash command-line would use one single arena that is shared by all the threads in Orthanc:

$ MALLOC_ARENA_MAX=1 ./Orthanc

Obviously, this restrictive setting will use minimal memory, but will result in contention among the threads. A good compromise might be to use 5 arenas:

$ MALLOC_ARENA_MAX=5 ./Orthanc

Memory allocation on GNU/Linux is a complex topic. There are other options available as environment variables that could also reduce memory consumption (for instance, MALLOC_MMAP_THRESHOLD_ would bypass arenas for large memory blocks such as DICOM files). Check out the manpage of mallopt() for more information.

Status:

  • Since Orthanc 1.8.2, the global configuration MallocArenaMax automatically sets MALLOC_MMAP_THRESHOLD_ (defaults to 5) during the startup of Orthanc.

  • The jodogne/orthanc and jodogne/orthanc-plugins Docker images automatically set MALLOC_ARENA_MAX to 5 since release 1.6.1 (cf. changeset).

  • The orthancteam/orthanc images automatically set MALLOC_ARENA_MAX to 5 since release 20.12.2.

Known limitations

Exclusive access to the DB in Orthanc <= 1.9.1

Orthanc was originally designed as a mini-DICOM server in 1-to-1 relation with a SQLite database. Until Orthanc 1.9.1, because of this original design, the internal code accessing the DB was affected by a strong limitation: Inside a single Orthanc process, there was no concurrent access to the DB.

One solution to avoid this limitation was to have multiple Orthanc accessing the same DB (works only for MySQL and PostgreSQL) as presented in this sample. However, this solution was only robust if there was one single “writer” Orthanc server (i.e. only one Orthanc was modifying the database). Indeed, the core of Orthanc <= 1.9.1 did not support the replay of database transactions, which is necessary to deal with conflicts between several instances of Orthanc that would simultaneously write to the database.

Concretely, in Orthanc <= 1.9.1, when connecting multiple Orthanc to a single database by setting Lock to false, there should only be one instance of Orthanc acting as a writer and all the other instances of Orthanc acting as readers only. Be careful to set the option SaveJobs to false in the configuration file of all the instances of Orthanc acting as readers (otherwise the readers would also modify the database).

Some issues reported in our bug tracker are related this limitation: issue 83, issue 121, issue 151.

This limitation has disappeared with Orthanc 1.9.2 and PostgreSQL/MySQL plugins 4.0, were the database engine was fully rewritten.

Concurrent accesses to the DB in Orthanc >= 1.9.2

In Orthanc 1.9.2 and PostgreSQL/MySQL plugins 4.0, the database engine of Orthanc was rewritten from scratch to allow multiple writers/readers to share the same database. This new feature necessitated a full refactoring of the database engine, so as to replay transactions in the case of collisions between concurrent transactions to the database.

Furthermore, one Orthanc server can also manage several connections to PostgreSQL or MySQL, in order to improve performance by adding concurrency. Read-only database transactions are also distinguished from read-write transactions in order for the database engine to further optimize the patterns of access.

Summarizing, the multiple readers/writers is now possible. Here is a drawing representing a possible deployment with 4 Orthanc servers, all sharing the same DICOM images, with some servers handling multiple connections to a PostgreSQL database for higher throughput:

../_images/2021-04-22-MultipleWriters.png

Care must be taken to the following aspects:

  • Orthanc 1.9.2 must be combined with a database plugin that supports multiple writers. This is the case of the PostgreSQL and MySQL plugins with version >= 4.0. The built-in SQLite database does not support multiple writers.

  • Concurrent access can result in so-called non-serializable transactions if two separate database transactions modify the database at the same time (cf. ErrorCode_DatabaseCannotSerialize in the source code of Orthanc). Orthanc will automatically replay such transactions a certain number of times (waiting 100ms more between each retry), until the transactions succeed. The plugins provide an option to control the maximum number of retries. If the maximum number of retries is exceeded, the 503 Service Unavailable HTTP error is raised (server overloaded because of unsuccessful retries of concurrent transactions). Note that, since version 6.0 of the PostgreSQL plugin, it is now possible to configure the TransactionMode to ReadCommitted instead of the default Serializable mode to avoid most of the transactions collisions. This option is not (yet) available for the MySQL plugin.

  • If a higher-level application modifies metadata and/or attachments in the presence of multiple writers, Orthanc provides a revision mechanism to prevent concurrent updates.

  • Thanks to this support of concurrent accesses, it is possible to put a load balancer on the top of the REST API of Orthanc. All the DICOM resources (patients, studies, series and instances) are indeed shared by all the instances of Orthanc connected to the same underlying database. As an application, this might be of great help if multiple viewers must connect to Orthanc. In Kubernetes, concurrent accesses also make it possible to manage a set of replicas of Orthanc (e.g. as deployment).

    There are however some caveats if using a load balancer or Kubernetes replicas, notably:

    • Each Orthanc instance maintains its own list of jobs. Therefore, the /jobs route will return only the jobs of the responding Orthanc.

    • Similarly, each Orthanc instance maintains its own status for the resources it has received. Thus, the IsStable information is local to each Orthanc instance.

    • The /modalities or the /peers are also private to each instance of Orthanc in the cluster, as soon as the respective options DicomModalitiesInDatabase and OrthancPeersInDatabase are set to true.

    If you need to use such primitives in your application, you have three possibilities: (1) Introduce a distinguished Orthanc server that is responsible to take care of all the jobs (including modalities and peers) and/or to receive all the DICOM instances, (2) create an Orthanc plugin (e.g. using Python or Java) that queries all the Orthanc in the cluster and that aggregates all of their answers, or (3) do the same using a higher-level framework (such as Node.js).

Latency

For some queries to the database, Orthanc performs several small SQL requests. For instance, a request to a route like /studies/{id} can trigger 6 SQL queries. Given these round-trips between Orthanc and the DB server, it’s important for the network latency to be as small as possible. For instance, if your latency is 20ms, a single request to /studies/{id} might take 120ms. Typically, a latency of 1-4 ms is expected to have correct performances.

As a consequence, if deploying Orthanc in a cloud infrastructure, make sure that the DB server and Orthanc VMs are located in the same datacenter. Note that most of the time-consuming queries have already been optimized, and that future versions of Orthanc SDK might aggregate even more SQL requests.

Starting with Orthanc 1.9.2, and PostgreSQL/MySQL index plugins 4.0, Orthanc can also be configured to handle multiple connections to the database server by setting the IndexConnectionsCount to a value greater than 1. This allows concurrent accesses to the database, which avoids to sequentially wait for a database transaction to be concluded before starting another one. Having multiple connections makes the latency problem much less important.

Slow deletions

Deleting large studies can take much time, because removing a large number of files from a filesystem can be an expensive operation (which might sound counter-intuitive). This is especially true with HDD drives, that can be much slower than SSD (an user has reported a 20 times speedup by switching from HDD to SSD).

If switching from HDD to SDD is not applicable, you may also use the Delayed Deletion plugin . The plugin would maintains a queue of files to be removed. The actual deletion from the filesystem is done asynchronously in a separate thread.