Nuxeo Server

Nuxeo Stream

Updated: May 17, 2018 Page Information Edit on GitHub

Nuxeo Stream provides a Log storage abstraction and a Stream processing pattern.

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Log and Stream Processing

A log is a storage abstraction, it is an append-only sequence of record ordered by time. This simple concept is at the heart of many data intensive applications. It brings fault tolerance, durability, immutability and ordering to distributed system.

When the processing of log records is expected to give near real time feedback and the number of records is unbounded this is called stream processing.

Nuxeo uses a pattern called computation for stream processing, it enables to compose producer/consumer into a complex topology.

Visit the nuxeo-stream README for more details.

Stream Library

The nuxeo-stream module provides a log based broker message passing system with a computation stream pattern.

This module has no dependency on Nuxeo framework to ease integration with third parties.

The underlying log solution relies:

  • either on Chronicle Queue which is a high performance off-Heap queue library, there is no broker to install, it relies entirely on OS memory mapped file.

  • either on Apache Kafka which is a distributed streaming platform.

The Chronicle Queue implementation can be used when producers and consumers are on the same node, for distributed support Kafka is needed.

Please visit the Kafka page for more information.

Nuxeo Stream

The nuxeo-runtime-stream module provides an integration of nuxeo-stream with Nuxeo by exposing two services:

Kafka Configuration Service

This service enable to register Kafka and Zookeeper access, along with Kafka properties for consumer and producers.

Please visit the Kafka page for more information.

Nuxeo Stream Service

This StreamService service provides:

  • A way to register different Log configurations. For instance you can define a Log based on Chronicle Queue with the storage location and a retention.

  • An access to a Log manager that can create Log, create tailer (reader) or appender (writer).

  • A way to register stream processing. By providing a class that define a topology of computations, the service will take care of creating a Pool of thread that run the processing.

Integration

The Nuxeo stream service is used in different locations.

Stream Audit Writer

Used by default since Nuxeo 9.3. The events that need to be traced in the audit are collected by a sync listener. When the transaction is committed, events are written as JSON into an audit Log. A StreamProcessor takes the audit Log as input and send the entries in batch to the Audit backend.

This brings:

  • Reliability: the entries are first persisted to the log and can be processed later even if the audit backend is not reachable or if the application crash.
  • Performance: the entries are sent to the audit backend at a constant and optimal throughput, not tied to the application activity.

The Stream audit writer can be configured with the following options:

# When disabled the previous "no stream" audit bulk writer implementation is used
nuxeo.stream.audit.enabled=true
# The log configuration to use
nuxeo.stream.audit.log.config=audit
# Send log entries by batch
nuxeo.stream.audit.batch.size=25
# Do not wait more than this threshold if the batch is not full
nuxeo.stream.audit.batch.threshold.ms=500

Stream WorkManager

This implementation is not activated by default.

Instead of queueing work into memory or into Redis (which is also in memory), work can be queued into a Log without worries about the memory limits.

Kafka is required to enable distributed work in cluster mode.

To scale horizontally, so adding Nuxeo node supports more load, the number of Log partitions that fix the maximum concurrency must be greater than the thread pool size of a single node. This strategy is called partition over provisioning.

By default there is an over provisioning factor of 3. For instance with a work pool of size 4, we have 12 partitions in the Log:

  • For a cluster of 1 node: there are 4 threads, each reading from 3 partitions.
  • For a cluster of 2 nodes: there are 8 threads, some reading from 2 partitions, other reading from 1 partition.
  • For a cluster of 3 nodes: there are 12 threads, each thread reading from one partition.
  • For a cluster of 4 nodes: there are 16 threads, some threads in the work pool will be unused, reducing the overall node load.

Note that work pool of size 1 are not over provisioned because we don't want any concurrency.

The Stream WorkManager can be configured with the following options:

# Activate the StreamWorkManager
nuxeo.stream.work.enabled=true
# The log config to use
nuxeo.stream.work.log.config=work
# The over provisioning factor
nuxeo.stream.work.over.provisioning.factor=3

The behavior of the Stream WorkManager is slightly different than the default WorkManager:

  • Works are immutable: they can not be used as a storage for result, the repository or the transient store should be used instead
  • Works can not be listed or loaded on demand because this can not scale
  • Works with the same id are executed only once, this make work idempotent for free
  • At the moment the number of Running works is only an estimation, scheduled and completed metrics are reliable

This requires some work and WorkManager usage adaptations, this is still a work in progress.

Stream Importer

The nuxeo-importer-stream which is part of the nuxeo-platform-importer addon use Log to run document importer.

Please visit nuxeo-importer-stream README for more information.

7 days ago manonlumeau Remove updated review date
History: Created by manonlumeau

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