Troubleshooting Common Problems

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Certain versions of the JRuby runtime and libraries in certain plugins (the Netty network library in the TCP input, for example) copy executable files to the temp directory. This situation causes subsequent failures when /tmp is mounted noexec.

Sample error

[2018-03-25T12:23:01,149][ERROR][org.logstash.Logstash ]
java.lang.IllegalStateException: org.jruby.exceptions.RaiseException:
(LoadError) Could not load FFI Provider: (NotImplementedError) FFI not
available: java.lang.UnsatisfiedLinkError: /tmp/jffi5534463206038012403.so:
/tmp/jffi5534463206038012403.so: failed to map segment from shared object:
Operation not permitted

Possible solutions

Running Logstash with Java 11 results in warnings similar to these:

WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.jruby.ext.openssl.SecurityHelper (file:/{...}/jruby{...}jopenssl.jar) to field java.security.MessageDigest.provider
WARNING: Please consider reporting this to the maintainers of org.jruby.ext.openssl.SecurityHelper
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release

These errors appear related to a known issue with JRuby.

Work around

Try adding these values to the jvm.options file.

--add-opens=java.base/java.security=ALL-UNNAMED
--add-opens=java.base/java.io=ALL-UNNAMED
--add-opens=java.base/java.nio.channels=org.jruby.dist
--add-opens=java.base/sun.nio.ch=org.jruby.dist
--add-opens=java.management/sun.management=org.jruby.dist

Notes:

A 429 message indicates that an application is busy handling other requests. For example, Elasticsearch sends a 429 code to notify Logstash (or other indexers) that the bulk failed because the ingest queue is full. Logstash will retry sending documents.

Possible actions

Check Elasticsearch to see if it needs attention.

Sample error

[2018-08-21T20:05:36,111][INFO ][logstash.outputs.elasticsearch] retrying
failed action with response code: 429
({"type"=>"es_rejected_execution_exception", "reason"=>"rejected execution of
org.elasticsearch.transport.TransportService$7@85be457 on
EsThreadPoolExecutor[bulk, queue capacity = 200,
org.elasticsearch.common.util.concurrent.EsThreadPoolExecutor@538c9d8a[Running,
pool size = 16, active threads = 16, queued tasks = 200, completed tasks =
685]]"})

For general performance tuning tips and guidelines, see Performance Tuning.

Pipelines, by definition, are unique. Here are some guidelines to help you get started.

For basic pipelines, this configuration could be enough to make the problem show itself.

input {stdin{}} output {stdout{}}

Logstash can separate logs by pipeline. This feature can help you identify the offending pipeline. Set pipeline.separate_logs: true in your logstash.yml to enable the log per pipeline feature.

For more complex pipelines, the problem could be caused by a series of plugins in a specific order. Troubleshooting these pipelines usually requires trial and error. Start by systematically removing input and output plugins until you’re left with the minimum set that manifest the issue.

We want to expand this section to make it more helpful. If you have troubleshooting tips to share, please:

Symptoms

Simple filters such as mutate or json filter can take several milliseconds per event to execute. Inputs and outputs might be affected, too.

Background

The different plugins running on Logstash can be quite verbose if the logging level is set to debug or trace. As the logging library used in Logstash is synchronous, heavy logging can affect performances.

Solution

Reset the logging level to info.

Symptoms

Throughput issues and duplicate event processing Logstash logs warnings:

[2017-10-18T03:37:59,302][WARN][org.apache.kafka.clients.consumer.internals.ConsumerCoordinator]
Auto offset commit failed for group clap_tx1: Commit cannot be completed since
the group has already rebalanced and assigned the partitions to another member.

The time between subsequent calls to poll() was longer than the configured session.timeout.ms, which typically implies that the poll loop is spending too much time processing messages. You can address this by increasing the session timeout or by reducing the maximum size of batches returned in poll() with max.poll.records.

[INFO][org.apache.kafka.clients.consumer.internals.ConsumerCoordinator] Revoking
previously assigned partitions [] for group log-ronline-node09
`[2018-01-29T14:54:06,485][INFO]`[org.apache.kafka.clients.consumer.internals.ConsumerCoordinator]
Setting newly assigned partitions [elk-pmbr-9] for group log-pmbr

Background

Kafka tracks the individual consumers in a consumer group (for example, a number of Logstash instances) and tries to give each consumer one or more specific partitions of data in the topic they’re consuming. In order to achieve this, Kafka tracks whether or not a consumer (Logstash Kafka input thread) is making progress on their assigned partition, and reassigns partitions that have not made progress in a set timeframe.

When Logstash requests more events from the Kafka Broker than it can process within the timeout, it triggers reassignment of partitions. Reassignment of partitions takes time, and can cause duplicate processing of events and significant throughput problems.

Possible solutions

Details

The max_poll_records option sets the number of records to be pulled in one request. If it exceeds the default value of 500, try reducing it.

The consumer_threads option sets the number of input threads. If the value exceeds the number of pipeline workers configured in the logstash.yml file, it should certainly be reduced. If the value is greater than 4, try reducing it to 4 or less if the client has the time/resources for it. Try starting with a value of 1, and then incrementing from there to find the optimal performance.

The session_timeout_ms option sets the relevant timeout. Set it to a value that ensures that the number of events in max_poll_records can be safely processed within the time limit.

EXAMPLE
Pipeline throughput is `10k/s` and `max_poll_records` is set to 1k =>. The value
must be at least 100ms if `consumer_threads` is set to `1`. If it is set to a
higher value `n`, then the minimum session timeout increases proportionally to
`n * 100ms`.

In practice the value must be set much higher than the theoretical value because the behavior of the outputs and filters in a pipeline follows a distribution. The value should also be higher than the maximum time you expect your outputs to stall. The default setting is 10s == 10000ms. If you are experiencing periodic problems with an output that can stall because of load or similar effects (such as the Elasticsearch output), there is little downside to increasing this value significantly to say 60s.

From a performance perspective, decreasing the max_poll_records value is preferable to increasing the timeout value. Increasing the timeout is your only option if the client’s issues are caused by periodically stalling outputs. Check logs for evidence of stalling outputs, such as ES output logging status 429.

Symptoms

Logstash’s Kafka Input is causing a much higher number of commits to the offset topic than expected. Often the complaint also mentions redundant offset commits where the same offset is committed repeatedly.

Solution

For Kafka Broker versions 0.10.2.1 to 1.0.x: The problem is caused by a bug in Kafka. https://issues.apache.org/jira/browse/KAFKA-6362 The client’s best option is upgrading their Kafka Brokers to version 1.1 or newer.

For older versions of Kafka or if the above does not fully resolve the issue: The problem can also be caused by setting the value for poll_timeout_ms too low relative to the rate at which the Kafka Brokers receive events themselves (or if Brokers periodically idle between receiving bursts of events). Increasing the value set for poll_timeout_ms proportionally decreases the number of offsets commits in this scenario. For example, raising it by 10x will lead to 10x fewer offset commits.

Symptoms

Logstash Kafka input randomly logs errors from the configured codec and/or reads events incorrectly (partial reads, mixing data between multiple events etc.).

Log example:  [2018-02-05T13:51:25,773][FATAL][logstash.runner          ] An
unexpected error occurred! {:error=>#<TypeError: can't convert nil into String>,
:backtrace=>["org/jruby/RubyArray.java:1892:in `join'",
"org/jruby/RubyArray.java:1898:in `join'",
"/usr/share/logstash/logstash-core/lib/logstash/util/buftok.rb:87:in `extract'",
"/usr/share/logstash/vendor/bundle/jruby/1.9/gems/logstash-codec-line-3.0.8/lib/logstash/codecs/line.rb:38:in
`decode'",
"/usr/share/logstash/vendor/bundle/jruby/1.9/gems/logstash-input-kafka-5.1.11/lib/logstash/inputs/kafka.rb:241:in
`thread_runner'",
"file:/usr/share/logstash/vendor/jruby/lib/jruby.jar!/jruby/java/java_ext/java.lang.rb:12:in
`each'",
"/usr/share/logstash/vendor/bundle/jruby/1.9/gems/logstash-input-kafka-5.1.11/lib/logstash/inputs/kafka.rb:240:in
`thread_runner'"]}

Background

There was a bug in the way the Kafka Input plugin was handling codec instances when running on multiple threads (consumer_threads set to > 1). https://github.com/logstash-plugins/logstash-input-kafka/issues/210

Solution

Coming soon