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SplunkLive! Hunk Technical Deep Dive
 

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  • On the first search, MapReduce auto-populates the Splunk binaries. The orchestration process begins by copying the Hunk binary .tgz file to HDFS. Hunk supports both the MapReduce JobTracker and the YARN MapReduce Resource Manager.Each TaskTracker (called ApplicationContainer in YARN) fetches the binary.TaskTrackers not involved in the 1st search will receive the Hunk binary in a subsequent search that involves those TaskTrackers. This process is one example of why Hunk needs some scratch space in HDFS and in the local file system (TaskTrackers / DataNodes). Hadoop NotesTypically a Hadoop cluster has a single master and multiple worker nodes. The master node (also referred to as NameNode) coordinates the reads and writes to worker nodes (also referred to as DataNodes). HDFS reliability is achieved by replicating the data across multiple machines. By default the replication value is 3 and chunk size is 64MB.The JobTracker dispatches tasks to worker nodes (TaskTracker) in the cluster. Priority is given to nodes that host the data upon which said task will operate on. If the task cannot be run on that node, next priority is given to neighboring nodes (in order to minimize network traffic). Upon job completion, each worker node writes own results locally and the HDFS ensures replication across the cluster.HDFS = NameNode + DataNodes
MapReduce Engine = JobTracker + TaskTracker
  • HDFS -> MapReduce -> (preprocess) -> Splunkd/TT -> MapReduce -> HDFS -> SH
  • Before data is processed by Hunk you can plug in your own data preprocessor. The preprocessors have to be written in Java and can transform the data in some way before Hunk gets a chance to. Data preprocessors can vary in complexity from simple translators (say Avro to JSON) to as complex as doing image/video/document processing.Hunk translates Avro to JSON. These translations happen on the fly and are not persisted.

SplunkLive! Hunk Technical Deep Dive Presentation Transcript

  • 1. Copyright © 2013 Splunk Inc. Hunk: Technical Deep Dive
  • 2. Legal Notices During the course of this presentation, we may make forward-looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward-looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not, be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described or to include any such feature or functionality in a future release. Splunk, Splunk>, Splunk Storm, Listen to Your Data, SPL and The Engine for Machine Data are trademarks and registered trademarks of Splunk Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. ©2013 Splunk Inc. All rights reserved. 2
  • 3. The Problem • Large amounts of data in Hadoop – Relatively easy to get the data in – Hard & time-consuming to get value out • Splunk has solved this problem before – Primarily for event time series • Wouldn’t if be great if Splunk could be used to analyze Hadoop data? Hadoop + Splunk = Hunk 3
  • 4. The Goals • A viable solution must: – – – – – Process the data in place Maintain support for Splunk Processing Language (SPL) True schema on read Report previews Ease of setup & use 4
  • 5. GOALS Support SPL • Naturally suitable for MapReduce • Reduces adoption time • Challenge: Hadoop “apps” written in Java & all SPL code is in C++ • Porting SPL to Java would be a daunting task • Reuse the C++ code somehow – Use “splunkd” (the binary) to process the data – JNI is not easy nor stable 5
  • 6. GOALS • Schema on Read Apply Splunk’s index-time schema at search time – Event breaking, time stamping etc. • Anything else would be brittle & maintenance nightmare • Extremely flexible • Runtime overhead (manpower >>$ computation) • Challenge: Hadoop “apps” written in Java & all index-time schema logic is implemented in C++ 6
  • 7. GOALS Previews • No one likes to stare at a blank screen! • Challenge: Hadoop is designed for batch-like jobs 7
  • 8. GOALS • Ease of Setup and Use Users should just specify: – Hadoop cluster they want to use – Data within the cluster they want to process • Immediately be able to explore and analyze their data 8
  • 9. Deployment Overview 9
  • 10. Move Data to Computation (stream) • Move data from HDFS to SH • Process it in a streaming fashion • Visualize the results • Problem? 10
  • 11. Move Computation to Data (MR) • Create and start a MapReduce job to do the processing • Monitor MR job and collect its results • Merge the results and visualize • Problem? 11
  • 12. Mixed Mode • Use both computation models concurrently preview Switch over time Stream preview MR Time 12
  • 13. First Search Setup hdfs://<working dir>/packages 1. Copy splunkd package HDFS .tgz Hunk search head > 2. Copy .tgz .tgz TaskTracker 1 TaskTracker 2 3. Expand in specified location on each TaskTracker 13 TaskTracker 3 4. Receives package in subsequent searches
  • 14. Data Flow 14
  • 15. Search Streaming Data Flow HDFS Raw data / data transfer Preprocessed data / stdin ERP Search process Final search results Results Search head 15
  • 16. Search TaskTracker Reporting Data Flow preprocessed raw HDFS MapReduce Search process Remote results Remote results Remote results Remote results ERP Search process Search head 16 Final search results Results
  • 17. Search DataNode/TaskTracker Raw data (HDFS) Data Processing Custom processing stdin You can plug in data preprocessors e.g. Apache Avro or format readers HDFS Inter. results Compress Indexing pipeline Event breaking Timestamping stdout Search pipeline Event typing Lookups Tagging Search processors Results splunkd/C++ MapReduce/Java 17 17
  • 18. Schematization Search Raw data Parsing Merging Typing IndexerPi pe splunkd/C++ 18 Search pipeline . . . Results
  • 19. Search – Search Head • Responsible for: – Orchestrating everything – Submitting MR jobs (optionally splitting bigger jobs into smaller ones) – Merging the results of MR jobs  Potentially with results from other VIXes or native indexes – Handling high level optimizations 19
  • 20. Optimization Partition Pruning Data is usually organized into hierarchical dirs, eg. /<base_path>/<date>/<hour>/<hostname>/somefile.log • Hunk can be instructed to extract fields and time ranges from a path • Ignores directories that cannot possibly contain search results • 20
  • 21. Optimization Partition Pruning e.g. Paths in a VIX: /home/hunk/20130610/01/host1/access_combined.log /home/hunk/20130610/02/host1/access_combined.log /home/hunk/20130610/01/host2/access_combined.log /home/hunk/20130610/02/host2/access_combined.log Search: index=hunk server=host1 Paths searched: /home/hunk/20130610/01/host1/access_combined.log /home/hunk/20130610/02/host1/access_combined.log 21
  • 22. Optimization Partition Pruning e.g. Paths in a VIX: /home/hunk/20130610/01/host1/access_combined.log /home/hunk/20130610/02/host1/access_combined.log /home/hunk/20130610/01/host2/access_combined.log /home/hunk/20130610/02/host2/access_combined.log Search: index=hunk earliest_time=“2013-06-10T01:00:00” latest_time =“2013-0610T02:00:00” Paths searched: /home/hunk/20130610/01/host1/access_combined.log /home/hunk/20130610/01/host2/access_combined.log 22
  • 23. Best Practices • Partition data in FS using fields that: – are commonly used – relatively low cardinality • For new data, use formats that are well defined, e.g. – Avro, JSON etc. – avoid columnar formats, like csv/tsv (hard to split) • Use compression, gzip, snappy etc. – I/O becomes a bottleneck at scale 23
  • 24. Troubleshooting Troubleshooting • search.log is your friend!!! • Log lines annotated with ERP.<name> … • Links for spawned MR job(s) Follow these links to troubleshoot MR issues • hdfs://<base_path>/dispatch/<sid>/<num>/<dispatch_dirs> contains the dispatch dir content of searches ran on TaskTracker 24
  • 25. Troubleshooting Job Inspector 25
  • 26. Troubleshooting Common Problems • User running Splunk does not have permission to write to HDFS or run MapReduce jobs • HDFS SPLUNK_HOME not writable • DN/TT SPLUNK_HOME not writable, out of disk • Data reading permission issues 26
  • 27. Helpful Resources • Download – http://www.splunk.com/bigdata • Help & docs – http://docs.splunk.com/Documentation/Hunk/6.0/Hunk/MeetHunk • Resource – http://answers.splunk.com 27
  • 28. Thank You