logback-kafka-appender

by danielwegener

danielwegener /logback-kafka-appender

Logback appender for Apache Kafka

470 Stars 187 Forks Last release: over 4 years ago (logback-kafka-appender-0.1.0) Apache License 2.0 132 Commits 8 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

logback-kafka-appender

Find help or join the discussion at https://gitter.im/danielwegener/logback-kafka-appender

Maven Central Build Status Coverage Status

This appender lets your application publish its application logs directly to Apache Kafka.

Logback incompatibility Warning

Due to a breaking change in the Logback Encoder API you need to use at least logback version 1.2.

Full configuration example

Add

logback-kafka-appender
and
logback-classic
as library dependencies to your project.
[maven pom.xml]

    com.github.danielwegener
    logback-kafka-appender
    0.2.0
    runtime


    ch.qos.logback
    logback-classic
    1.2.3
    runtime

// [build.sbt]
libraryDependencies += "com.github.danielwegener" % "logback-kafka-appender" % "0.2.0"
libraryDependencies += "ch.qos.logback" % "logback-classic" % "1.2.3"

This is an example

logback.xml
that uses a common
PatternLayout
to encode a log message as a string.
[src/main/resources/logback.xml]


<appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
    <encoder>
        <pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
    </encoder>
</appender>

<!-- This is the kafkaAppender -->
<appender name="kafkaAppender" class="com.github.danielwegener.logback.kafka.KafkaAppender">
        <encoder>
            <pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n</pattern>
        </encoder>
        <topic>logs</topic>
        <keyingstrategy class="com.github.danielwegener.logback.kafka.keying.NoKeyKeyingStrategy"></keyingstrategy>
        <deliverystrategy class="com.github.danielwegener.logback.kafka.delivery.AsynchronousDeliveryStrategy"></deliverystrategy>

        <!-- Optional parameter to use a fixed partition -->
        <!-- <partition>0</partition> -->

        <!-- Optional parameter to include log timestamps into the kafka message -->
        <!-- <appendTimestamp>true</appendTimestamp> -->

        <!-- each <producerConfig> translates to regular kafka-client config (format: key=value) -->
        <!-- producer configs are documented here: https://kafka.apache.org/documentation.html#newproducerconfigs -->
        <!-- bootstrap.servers is the only mandatory producerConfig -->
        <producerconfig>bootstrap.servers=localhost:9092</producerconfig>

        <!-- this is the fallback appender if kafka is not available. -->
        <appender-ref ref="STDOUT"></appender-ref>
    </appender>

<root level="info">
    <appender-ref ref="kafkaAppender"></appender-ref>
</root>

You may also look at the complete configuration examples

Compatibility

logback-kafka-appender depends on

org.apache.kafka:kafka-clients:1.0.0:jar
. It can append logs to a kafka broker with version 0.9.0.0 or higher.

The dependency to kafka-clients is not shadowed and may be upgraded to a higher, api compatible, version through dependency overrides.

Delivery strategies

Direct logging over the network is not a trivial thing because it might be much less reliable than the local file system and has a much bigger impact on the application performance if the transport has hiccups.

You need make a essential decision: Is it more important to deliver all logs to the remote Kafka or is it more important to keep the application running smoothly? Either of this decisions allows you to tune this appender for throughput.

| Strategy | Description | |---|---| |

AsynchronousDeliveryStrategy
| Dispatches each log message to the
Kafka Producer
. If the delivery fails for some reasons, the message is dispatched to the fallback appenders. However, this DeliveryStrategy does block if the producers send buffer is full (this can happen if the connection to the broker gets lost). To avoid even this blocking, enable the producerConfig
block.on.buffer.full=false
. All log messages that cannot be delivered fast enough will then immediately go to the fallback appenders. | |
BlockingDeliveryStrategy
| Blocks each calling thread until the log message is actually delivered. Normally this strategy is discouraged because it has a huge negative impact on throughput. Warning: This strategy should not be used together with the producerConfig
linger.ms
|

Note on Broker outages

The

AsynchronousDeliveryStrategy
does not prevent you from being blocked by the Kafka metadata exchange. That means: If all brokers are not reachable when the logging context starts, or all brokers become unreachable for a longer time period (>
metadata.max.age.ms
), your appender will eventually block. This behavior is undesirable in general and can be migitated with kafka-clients 0.9 (see #16). Until then, you can wrap the KafkaAppender with logback's own AsyncAppender.

An example configuration could look like this:

<!-- This is the kafkaAppender -->
<appender name="kafkaAppender" class="com.github.danielwegener.logback.kafka.KafkaAppender">
<!-- Kafka Appender configuration -->
</appender>

<appender name="ASYNC" class="ch.qos.logback.classic.AsyncAppender">
    <appender-ref ref="kafkaAppender"></appender-ref>
</appender>

<root level="info">
    <appender-ref ref="ASYNC"></appender-ref>
</root>

Custom delivery strategies

You may also roll your own delivery strategy. Just extend

com.github.danielwegener.logback.kafka.delivery.DeliveryStrategy
.

Fallback-Appender

If, for whatever reason, the kafka-producer decides that it cannot publish a log message, the message could still be logged to a fallback appender (a

ConsoleAppender
on STDOUT or STDERR would be a reasonable choice for that).

Just add your fallback appender(s) as logback

appender-ref
to the
KafkaAppender
section in your
logback.xml
. Every message that cannot be delivered to kafka will be written to all defined
appender-ref
's.

Example:

 while 
STDOUT
is an defined appender.

Note that the

AsynchronousDeliveryStrategy
will reuse the kafka producers io thread to write the message to the fallback appenders. Thus all fallback appenders should be reasonable fast so they do not slow down or break the kafka producer.

Producer tuning

This appender uses the kafka producer introduced in kafka-0.8.2. It uses the producer default configuration.

You may override any known kafka producer config with an

Name=Value
block (note that the
boostrap.servers
config is mandatory). This allows a lot of fine tuning potential (eg. with
batch.size
,
compression.type
and
linger.ms
).

Serialization

This module supports any

ch.qos.logback.core.encoder.Encoder
. This allows you to use any encoder that is capable of encoding an
ILoggingEvent
or
IAccessEvent
like the well-known logback
PatternLayoutEncoder
or for example the logstash-logback-encoder's
LogstashEncoxer
.

Custom Serialization

If you want to write something different than string on your kafka logging topic, you may roll your encoding mechanism. A use case would be to to smaller message sizes and/or better serialization/deserialization performance on the producing or consuming side. Useful formats could be BSON, Avro or others.

To roll your own implementation please refer to the logback documentation. Note that logback-kafka-appender will never call the

headerBytes()
or
footerBytes()
method.

Your encoder should be type-parameterized for any subtype of the type of event you want to support (typically

ILoggingEvent
) like in
public class MyEncoder extends ch.qos.logback.core.encoder.Encoder {/*..*/}

Keying strategies / Partitioning

Kafka's scalability and ordering guarantees heavily rely on the concepts of partitions (more details here). For application logging this means that we need to decide how we want to distribute our log messages over multiple kafka topic partitions. One implication of this decision is how messages are ordered when they are consumed from a arbitrary multi-partition consumer since kafka only provides a guaranteed read order only on each single partition. Another implication is how evenly our log messages are distributed across all available partitions and therefore balanced between multiple brokers.

The order of log messages may or may not be important, depending on the intended consumer-audience (e.g. a logstash indexer will reorder all message by its timestamp anyway).

You can provide a fixed partition for the kafka appender using the

partition
property or let the producer use the message key to partition a message. Thus
logback-kafka-appender
supports the following keying strategies strategies:

| Strategy | Description | |---|---| |

NoKeyKeyingStrategy
(default) | Does not generate a message key. Results in round robin distribution across partition if no fixed partition is provided. | |
HostNameKeyingStrategy
| This strategy uses the HOSTNAME as message key. This is useful because it ensures that all log messages issued by this host will remain in the correct order for any consumer. But this strategy can lead to uneven log distribution for a small number of hosts (compared to the number of partitions). | |
ContextNameKeyingStrategy
| This strategy uses logback's CONTEXT_NAME as message key. This is ensures that all log messages logged by the same logging context will remain in the correct order for any consumer. But this strategy can lead to uneven log distribution for a small number of hosts (compared to the number of partitions). This strategy only works for
ILoggingEvents
. | |
ThreadNameKeyingStrategy
| This strategy uses the calling threads name as message key. This ensures that all messages logged by the same thread will remain in the correct order for any consumer. But this strategy can lead to uneven log distribution for a small number of thread(-names) (compared to the number of partitions). This strategy only works for
ILoggingEvents
. | |
LoggerNameKeyingStrategy
| * This strategy uses the logger name as message key. This ensures that all messages logged by the same logger will remain in the correct order for any consumer. But this strategy can lead to uneven log distribution for a small number of distinct loggers (compared to the number of partitions). This strategy only works for
ILoggingEvents
. |

Custom keying strategies

If none of the above keying strategies satisfies your requirements, you can easily implement your own by implementing a custom

KeyingStrategy
:
package foo;
import com.github.danielwegener.logback.kafka.keying.KeyingStrategy;

/* This is a valid example but does not really make much sense */ public class LevelKeyingStrategy implements KeyingStrategy { @Override public byte[] createKey(ILoggingEvent e) { return ByteBuffer.allocate(4).putInt(e.getLevel()).array(); } }

As most custom logback component, your custom partitioning strategy may also implement the

ch.qos.logback.core.spi.ContextAware
and
ch.qos.logback.core.spi.LifeCycle
interfaces.

A custom keying strategy may especially become handy when you want to use kafka's log compactation facility.

FAQ

  • Q: I want to log to different/multiple topics!
    A: No problem, create an appender for each topic.

License

This project is licensed under the Apache License Version 2.0.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.