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Big Data Open Source Security LLC: Realtime log analysis with Mesos, Docker, Kafka, Spark, Cassandra and Solr at scale
This document discusses real-time log analysis using Mesos, Docker, Kafka, Spark, Cassandra and Solr at scale. It provides an overview of the architecture, describing how data from various sources like syslog can be ingested into Kafka via Docker producers. It then discusses consuming from Kafka to write to Cassandra in real-time and running Spark jobs on Cassandra data. The document uses these open source tools together in a reference architecture to enable real-time analytics and search capabilities on streaming data.
Big Data Open Source Security LLC: Realtime log analysis with Mesos, Docker, Kafka, Spark, Cassandra and Solr at scale
- 1. Real-Time log analysiswith Mesos, Docker, Kafka, Spark, Cassandra and Solr at scale
- 2. whoami CEO of Elodinahttp://www.elodina.net/ a big data as a service platform built on top open source software. The Elodina platform enables customers to analyze data streams and programmatically react to the results in real-time. We solve today’s data analytics needs by providing the tools and support necessary to utilize open source technologies. As users, contributors and committers, Elodina also provides support for frameworks that run on Mesos including Apache Kafka, Exhibitor (Zookeeper), Apache Storm, Apache Cassandra and a whole lot more! Apache Kafka Committer & PMC Member LinkedIn: http://linkedin.com/in/charmalloc Twitter : @allthingshadoop 2© 2015. All Rights Reserved.
- 3. 1 Intro ToMesos, Kafka, Etc 2 Architecture Overview 3 Breaking it down into pieces 4 Questions? 3© 2015. All Rights Reserved.
- 4. Apache Mesos 4© 2015.All Rights Reserved.
- 5. Mesos Papers Mesos: APlatform for Fine-Grained Resource Sharing in the Data Center http://static.usenix.org/event/nsdi11/tech/full_papers/Hindman_new.pdf Google Borg - https://research.google.com/pubs/pub43438.html Google Omega: flexible, scalable schedulers for large compute clusters http: //eurosys2013.tudos.org/wp-content/uploads/2013/paper/Schwarzkopf.pdf 5
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- 10. Fine Grained ResourceElasticity "If people knew how low it really is, we’d all get fired." https://gigaom.com/2013/11/30/the-sorry-state-of-server-utilization-and-the-impending-post-hypervisor-era/ 10
- 11. An operating systemfor your data center 11
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- 14. Marathon 14 https://github.com/mesosphere/marathon Cluster-wide init andcontrol system for services in cgroups or docker based on Apache Mesos
- 15. Docker on Marathon { "id":"basic-3", "cmd": "python3 -m http.server 8080", "cpus": 0.5, "mem": 32.0, "container": { "type": "DOCKER", "docker": { "image": "python:3", "network": "BRIDGE", "portMappings": [ { "containerPort": 8080, "hostPort": 0 } ] } } } 15
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- 17. Kafka papers Apache Kafkawas first open sourced by LinkedIn in 2011 Papers ● Building a Replicated Logging System with Apache Kafka http://www.vldb.org/pvldb/vol8/p1654-wang.pdf ● Kafka: A Distributed Messaging System for Log Processing http://research.microsoft.com/en- us/um/people/srikanth/netdb11/netdb11papers/netdb11-final12.pdf ● Building LinkedIn’s Real-time Activity Data Pipeline http://sites.computer.org/debull/A12june/pipeline.pdf ● The Log: What Every Software Engineer Should Know About Real-time Data's Unifying Abstraction http://engineering. linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying http://kafka.apache.org/ 17
- 18. How Big DataStarts 18
- 19. More Big Data!More! 19
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- 22. Kafka de-couples datapipelines 22
- 23. Distributed Replicated Log Read& Write In real time As much as you want As fast as your network 23
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- 25. Producers syslog → Kafkavia docker https://hub.docker.com/r/stealthly/syslog/ syslog → Kafka scheduler https://github.com/stealthly/syslog-service statsd → Kafka scheduler https://github.com/stealthly/statsd-mesos-kafka system stats collection → Kafka scheduler https://github.com/stealthly/syscol tailf → Kafka https://github.com/stealthly/go_kafka_client/tree/master/producers/tailf Any language https://cwiki.apache.org/confluence/display/KAFKA/Clients 25
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- 28. Kafka on Mesos ●smart broker.id assignment. ● preservation of broker placement (through constraints and/or new features). ● ability to-do configuration changes. ● rolling restarts (for things like configuration changes). ● scaling the cluster up and down with automatic, programmatic and manual options. ● smart partition assignment via constraints visa vi roles, resources and attributes. 28
- 29. CLI & RESTAPI ● scheduler - starts the scheduler. ● broker ○ add - adds one more more brokers to the cluster. ○ update - changes resources, constraints or broker properties one or more brokers. ○ remove - take a broker out of the cluster. ○ start - starts a broker up. ○ stop - this can either a graceful shutdown or will force kill it (./kafka-mesos.sh help stop) ● topic ○ list - list topics in cluster ○ add - add new topics in cluster ○ update - change topics in cluster ○ rebalance - allows you to rebalance a cluster either by selecting the brokers or topics to rebalance. Manual assignment is still possible using the Apache Kafka project tools. Rebalance can also change the replication factor on a topic. ● help - ./kafka-mesos.sh help || ./kafka-mesos.sh help {command} 29
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- 31. Schema Avro orProtoBuff - https://github.com/stealthly/go_kafka_client/blob/master/syslog/syslog_proto/logline.proto - https://github.com/stealthly/go_kafka_client/blob/master/logline.avsc logline • line • logtypeid • source • tags (k/v pairs) • timings (k/v pairs) 31
- 32. Consume from Kafka→ Write to Cassandra Implement CQL write here https://github. com/stealthly/go_kafka_client/blob/master/consumers/consum ers.go#L186-L194 with https://github.com/gocql/gocql Go Kafka Client does fan out work processing, rebalance doesn’ t upset consumers that are reading already. 32
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- 34. Sample Spark Job→ Cassandra https://github.com/stealthly/gauntlet Uses the Cassandra Spark Connector https://github. com/datastax/spark-cassandra-connector 34
- 35. Use DataStax Enterpriseto enable Search http://docs.datastax.com/en/datastax_enterprise/4. 8/datastax_enterprise/srch/srchOverview.html 35
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