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Case Study: Elasticsearch Ingest Using StreamSets at Cisco Intercloud

  1. 1. Case Study Elasticsearch Ingest @ Cisco Intercloud
  2. 2. Agenda • Express Overview of StreamSets Data Collector Kirit Basu, Product Management, StreamSets • Introduction to Elastic CatherineJohnson, Solutions Architect, Elastic • Implementing Shipped Analytics Using StreamSets and Elasticsearch Dmitri Chtchourov, Innovation Architect, Cloud Solutions CTO Group Group
  3. 3. Performance Management for Data Flows
  4. 4. © 2015 StreamSets, Inc. All rights reserved. May not be copied, modified, or distributed in whole or part without written consent of StreamSets, Inc. History Founded by Informatica and Cloudera veterans. Mission Bring operational excellence to managing data in motion. Challenge Move data efficiently and with quality in the face of change. Solution Open source software enabling performance management of data flows. Use cases Hadoop Ingest, Search Ingest, Message Broker Enablement, Log Shipping, Cloud Migration, IoT, ... Momentum Thousands of downloads, hundreds of companies using. StreamSets At a Glance
  5. 5. © 2015 StreamSets, Inc. All rights reserved. May not be copied, modified, or distributed in whole or part without written consent of StreamSets, Inc. StreamSets Data Collector Adaptable Flows for Efficiency Design ingest pipelines with minimal coding and maximum flexibility. Data Flow KPIs for Control Monitor and act on data flow performance and data quality. Containerized Architecture for Agility Operate continuously in the face of constant change. Open source software for the rapid development and reliably operation of complex data flows.
  6. 6. Get Started with StreamSets http://streamsets.com/opensource https://github.com/streamsets/datacollector/ #streamsets
  7. 7. March 2016 Introduction to Elastic
  8. 8. Software that makes massive amounts of structured and unstructured data usable for search, logging, analytics, and more in mission critical systems and applications
  9. 9. Examples: Elastic Stack Use Cases Logging IT Operations Application Management Security Analytics Analytics Search Marketing Insights Business Development Customer Sentiment Website Search Internal/Intranet Search URL Search Internal Systems/Applications External Systems/Applications Developers IT/Ops Business Users
  10. 10. Elastic Solves Many Developer Use Cases Social Location User- Activity Machine (Log files) Documents Handles Complex & Diverse Data Meets Today’s Core Developer Requirements Developer requirements Many users / use cases Fast data processing Large data volumes Data quality & integrity Cross-source insights Solves Critical Use Cases Application Search Embedded Search Logging Security Analytics Operational Analytics More …
  11. 11. The Elastic Stack Ingest Store, Index, & Analyze User Interface Plugins Monitoring Security Alerting Elastic Cloud: Hosted Elasticsearch
  12. 12. Thank you! www.elastic.co
  13. 13. Implementing Shipped Analytics Using Streamsets and Elasticsearch Dmitri Chtchourov, Innovation Architect, Cloud Solutions CTO Group Tymofii Polekhin, Software Engineer
  14. 14. Agenda MANTL & Shipped Shipped Analytics for Shipped Why we need Shipped Analytics? Archtecture and Data Flow Streamsets Pipelines End to end dataflow and performance with Elasticsearch Benefits of Streamsets Demo
  15. 15. Microservices managed and scaled separately Microservices managed by Mesos in a single platform Microservices architecture for Mesos frameworks and other components CIS/AWS/Metastack/vSphere/UCS… Terraform Spark Executor N Spark Executor 1 Spark Scheduler Kafka Broker N Kafka Broker 1 Kafka Scheduler Docker Docker TraefikMicroservices … REST API REST API Scripted provisioning Direct provisioning Policy, Auto-scaling VM1 or BM1 VM2 or BM2 VM3 or BM3 VM4 or BM4 VM5 or BM5
  16. 16. Shipped Analytics Cluster Probe Probe Probe • Both Shipped and Shipped Analytics running on MANTL • Shipped Analytics – infra and app logs and metrics analysis
  17. 17. mesos-master mesos-slave marathon zookeeper consul syslog frameworks collectd cpu memory interface disk df load docker zookeeper marathon mesos-slave mesos-master CollectD and Filebeat processes running on every node in the cluster.
  18. 18. Infrastructure Layer Zookeeper Cluster Consul Cluster Mesos Cluster Marathon Framework Kafka Cluster topbeat filebeat journalbeat dockerbeat • Experimenting with Elastic Beats (unified arch., closer to micro-services model) • Elastic Beats to replace collectd plugins and cAdvisor for containers
  19. 19. <file | top | *>beat collectd logstash DNS SRV beats.logstash.service.consul Data normalization Tagging Cluster name decoration Logstash is a single process per cluster, discoverable with standard inter-cluster discovery mechanism, which will get metrics from collectd on every slave and logs from filebeat on every slave, normalize data and send to desired output DNS SRV collectd.logstash.service.consul NOTE: currently Logstash is running in Docker container on every node, will be moving to Filebeat and Logstash mesos framework soon
  20. 20. logstash Kafka 0.9.0.0 supports SSL authentication and data encryption for producers. This is must-have security when sending data to external destination through WAN. Sending data to central SA cluster for long-term analytics SSL encryption WAN kafka SSL authentication Shipped cluster Shipped Analytics
  21. 21. StreamSets running in Mesos Spark Cluster mode processing data from multiple source Shipped clusters and storing it in Elasticsearch cluster. kafka elasticsearch Streamsets Spark Streaming Cluster Spark Job Master instance Spark Job Spark Job Spark Job
  22. 22. Lambda Reference Architecture Monitoring / Analytics Cluster (local, Texas-3) Global Monitoring / Analytics Cluster (global, Texas-1) Monitoring / Analytics Cluster (local, Ams. -1 ) Monitoring / Analytics Cluster (local, Lon.-1) Local components and deployment is the same as global, just smaller Real-time and batch processing (Lambda), anomaly detection, visualization SSL Kafka SSL SSL MQTT
  23. 23. Divide nodes by role for more stable cluster operation and ease of scalability 3 master/search nodes 5 live data nodes 3 archive data nodes master/ search master/ search master/ search live/ data live/ data live/ data live/ data live/ data archive /data archive /data archive /data Shards=5 Replicas=4 Shards=5 Replicas=1 archive /data archive /data CPU=4 RAM=30GB HDD=4TB CPU=4 RAM=30GB HDD=4TB CPU=4 RAM=30GB HDD=4TB
  24. 24. Streamsets pipelines process incoming messages and transform them according to business logic requirements, normalizing metrics and parsing log lines; popping up important information using GROK filters or scripts. Cluster Name Decorator Fields Type Normalization Metrics/Logs Stream Splitter ES Logs Output General GROK Filters Float Value Truncate ES Metrics Output Shipped GROK Logic
  25. 25. Marathon • Streamsets instances running in docker containers in Marathon o Easy deployment and scaling o Fast upgrade to newer version • Issues we faced with this approach: o Containers were killed by marathon o Needed to re-import pipeline every time we launch container
  26. 26. Marathon • Working with Streamsets trying to resolve the OOM issue we increased container memory and SDC heap size • At first, all looked normal and we thought that it was just starving on resources, but several days later we had SDC killed again • We increased MEM and HEAP even more – to 16G, but we bought just another day or two before is was killed again • Looked like SDC heap were constantly filling with data that don’t go away and eventually it kills the container • Also GC was working hard and sometimes we got freezes up to 60 seconds • Decided to move out from Docker
  27. 27. Marathon • Streamsets reading JSON messages from Kafka cluster and output to Elasticsearch cluster o De-serializing and serializing JSON was very slow with single threaded process o Consuming from Kafka performance test showed:  JSON format: 5k records/sec avg  Text format: 50k records/sec avg  Binary format: 250k records/sec avg • Streamsets team were very proactive with this issues and in 2 days we received a fix for multi-threaded JSON parsing o New testing showed:  JSON format: 66k records/sec avg
  28. 28. Marathon • Streamsets has never failed because of any internal logic bugs but we kept seeing this oom-killer popping up and recovering was not automated • We decided to leave docker and run SDC natively on host, still using Marathon for scaling and failover • Without docker, we now can upload our pipeline on SDC startup, and it will start working as soon as instance has loaded We can freely scale up/down whenever we need Also, we got rid of oom-killer issue as well
  29. 29. Each one of our 3 SDC instances already processes ~3B messages, with no issues!
  30. 30. • Streamsets pipeline consume metrics gathered by collectd and logs gathered by logstash from 4 different clusters (including self), transform and decorate them and send to Elasticsearch for storage and analytics. • First of all we consume messages from Kafka topic at average of 5,000 messages per second. The consumer itself parses JSON-format and sends further. • Next stage is a JavaScript script that decorates messages with cluster name, based on a instance hostname in that message • Finally, we exclude Marathon events from stream sending them directly to ES
  31. 31. • Next stage will splits stream into 2 parts: logs and metrics • Metrics are send straight to ES without any transformation • Logs are the most interesting part: o We pop docker container logs from stream and delete “time” field that’s duplicate timstamp and sending them to ES o We separate logs from specific clusters, because we need to apply special logic for them o Separation is done though mapping IP’s to clusters in the pipeline realtime
  32. 32. • Collecting data from several Mesos clusters and need to correlate container metrics with it’s logs • Use appID taskID and runID to identify specific containers logs • Container logs itself have all three of this, while mesos- master and mesos-agent logs lacks runID • All unidentified data is discarded
  33. 33. Current ShippedAnalytics prod cluster configuration: Kafka Cluster: 7 brokers with 4CPU and 16GB RAM each Logstash topic for all incoming messages with 7 partitions and 2 replicas Current data flow is avg 5000 messages/sec to Kafka Current data size is avg 1,2MB/sec to Kafka Streamsets: 3 instances with identical pipeline configuration reading from Kafka cluster 7 partitions are split between 3 instances like 3/2/2 All 3 instances running natively on host (non-docker) with Marathon Marathon restarts failed instance with automatic pipeline upload and start Elasticsearch: 7 nodes with 4CPU, 16GB RAM and 2TB storage each Each metrics is written to its own index, total of 15 indexes Each index has 5 primary shards and 5 replica shards Total Doc count: 17,5B Total Doc size: 3.84TB 1 Day rate count: ~500M 1 Day rate size: ~120GB
  34. 34. Streamsets is a great product to work with, also team is super helpful and works fast • Lots of input and output connectors, huge processing capabilities • Very intuitive and rich User Interface • Easy to create pipelines visually, instead of writing code • Clear data flow paths • Small resource consumption compared to performance • Easily can handle up to 10k records/sec to Elasticsearch with 1CPU 2GB RAM • Simple configuration and deployment process • Opensource(!) • Fast logic changes with minimum downtime • Preview mode(!) – check every stage before throwing all your data it • Rich data transformation possibilities • GROK filters – easy to migrate from Logstash • Smart Errors handling • Reliable: not once did Streamets crashed by itself – only Docker, Marathon, Mesos issues
  35. 35. Thank You!

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