I use Kafka for message queue/processing. My question is about performance/best practice. I will do my own performance tests but maybe someone has results/experience already.
The data is raw in a Kafka (0.10) topic and I want to transfer it structured to ES and HDFS.
Now I see 2 possibilities:
Logstash (Kafka input plugin, grok filter (parsing), ES/webhdfs output plugin)
Kafka Streams (parsing), Kafka Connect (ES sink, HDFS sink)
Without any tests I would say that the second option is better/cleaner and more reliable?
Logstash "best practice" for getting data into Elasticsearch. WebHDFS won't have the raw performance of the Java API that is part of the Kafka Connect plugin, however.
Grok could be done in a Kafka Streams process, so your parsing could be done in either location.
If you are on an Elastic subscription, then they would like to sell Logstash. Confluent would like to sell Kafka Streams + Kafka Connect.
Avro seems to be the best medium for data transfer, and the Schema Registry is a popular way to do that. IIUC, Logstash doesn't work well with a Schema Registry or Avro, and prefers JSON.
In the Hadoop landscape, I would offer the intermediate options of Apache Nifi or Streamsets.
In the end, it really depends on your priorities, and how well you (and your team) can support these tools.
Related
Is is possible to use a single Kafka instance with the Elasticsearch Sink Connector to write to separate Elasticsearch clusters with the same index? Documentation. The source data may be a backend database or an application. An example use-case is that one cluster may be used for real-time search and the other may be used for analytics.
If this is possible, how do I configure the sink connector? If not, I can think of a couple of options:
Use 2 Kafka instances, each pointing to a different Elasticsearch cluster. Either write to both, or write to one and copy from it to the other.
Use a single Kafka instance and write a stream processor which will write to both clusters.
Are there any others?
Yes you can do this. You can use a single Kafka cluster and single Kafka Connect worker.
One connector can write to one Elasticsearch instance, and so if you have multiple destination Elasticsearch you need multiple connectors configured.
The usual way to run Kafka Connect is in "distributed" mode (even on a single instance), and then you submit one—or more—connector configurations via the REST API.
You don't need a Java client to use Kafka Connect - it's configuration only. The configuration, per connector, says where to get the data from (which Kafka topic(s)) and where to write it (which Elasticsearch instance).
To learn more about Kafka Connect see this talk, this short video, and this specific tutorial on Kafka Connect and Elasticsearch
I have to build a log-cluster and monitoring cluster ( For high-availability ) like this topology. I'm wondering to know how to config those log-shippers clusters. ( I have 2 Topo in the Image)
If I use Kafka with FileBeat in Kafka Cluster, Will Elastic Search
receive duplication data because Kafka has replicas in data?
If I use Logstash (In Elastic Search Cluster) for getting logs from
Kafka Cluster, how the config should be because I think that
Logstash will not know where to read the log efficiency on Kafka
Cluster.
Cluster topology
Thanks for reading. If you have any idea, please discuss with me ^^!
As i see both configurations are compatible with Kafka, you can use filebeat, logstash or mixed them in consumer and producer stages!
IMHO all depends about your needs, ie: sometimes we use some filters to rich the data before ingest to kafka (producer stage), or before index the data to elastic (consumer stage), in this case is better work with logsatsh, because is easier using filters than in filebeat
But if you want to play with raw data, maybe filebeat is betther, because the agent is lighter.
About your questions:
Kafka has the data replicted, but for HA propouses, you only read one time the data with the same consumer group
For read the log from kafka with logstash, you can use the logstash input plugin for kafka, is easy and works fine!
https://www.elastic.co/guide/en/logstash/current/plugins-inputs-kafka.html
I am looking for the light-weight log shipper which can directly transfer my logs to elasticsearch from kafka. Out of Filebeat, Logagent, Logstash(but i need light weighted) which among them or others can suites my use-case the best?
rsyslog is lightweight. As from version 8.27, it supports kafka as input. Elasticsearch as output is supported from even earlier.
Kafka input module configuration is described here
Elasticsearch output module configuration is described here
Someone suggested that Hadoop does streaming, and have quoted Flume and Kafka as examples.
While I understand they might have streaming features, I wonder if they can be considered in the same league as stream processing technologies like Storm/Spark/Flink. Kafka is a 'publish-subscribe model messaging system' and Flume is a data ingestion tool. And even though they interact/integrae with hadoop are they technically part of 'hadoop' itself?
PS: I understand there is a Hadoop Streaming which is an entirely different thing.
Hadoop is only YARN, HDFS, and MapReduce. As a project, it does not accommodate (near) real time ingestion or processing.
Hadoop Streaming is a tool used to manipulate data between filesystem streams (standard input/output)
Kafka is not only a publish/subscribe message queue.
Kafka Connect is essentially a Kafka channel, in Flume terms. Various plug-ins exist for reading from different "sources", producing to Kafka, then "sinks" exist to consume from Kafka to databases or filesystems. From a consumer perspective, this is more scalable than singular Flume agents deployed across your infrastructure. If all you're looking for log ingestion into Kafka, personally I find Filebeat or Fluentd to be better than Flume (no Java dependencies).
Kafka Streams is a comparable product to Storm, Flink, and Samza, except the dependency upon YARN or any cluster scheduler doesn't exist, and it's possible to embed a Kafka Streams processor within any JVM compatible application (for example, a Java web application). You'd have difficulties trying to do that with Spark or Flink without introducing a dependency on some external system(s).
The only benefits of Flume, NiFi, Storm, Spark, etc. I find is that they compliment Kafka and they have Hadoop compatible integrations along with other systems used in the BigData space like Cassandra (see SMACK stack)
So, to answer the question, you need to use other tools to allow streaming data to be processed and stored by Hadoop.
I'm looking to consume from Kafka and save data into Hadoop and Elasticsearch.
I've seen 2 ways of doing this currently: using Filebeat to consume from Kafka and send it to ES and using Kafka-Connect framework. There is a Kafka-Connect-HDFS and Kafka-Connect-Elasticsearch module.
I'm not sure which one to use to send streaming data. Though I think that if I want at some point to take data from Kafka and place it into Cassandra I can use a Kafka-Connect module for that but no such feature exists for Filebeat.
Kafka Connect can handle streaming data and is a bit more flexible. If you are just going to elastic, Filebeat is a clean integration for log sources. However, if you are going from Kafka to a number of different sinks, Kafka Connect is probably what you want. I'd recommend checking out the connector hub to see some examples of open source connectors at your disposal currently http://www.confluent.io/product/connectors/