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Adding Search to the Hadoop Ecosystem

Adding Search to the Hadoop Ecosystem. Gregory Chanan ( gchanan AT cloudera.com) LA HUG Sept 2013. Agenda. Big Data and Search – setting the stage Cloudera Search Architecture Component deep dive Security Conclusion. Why Search?. Hadoop for everyone Typical case:

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Adding Search to the Hadoop Ecosystem

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  1. Adding Search to the • Hadoop Ecosystem Gregory Chanan (gchanan AT cloudera.com) LA HUG Sept 2013

  2. Agenda • Big Data and Search – setting the stage • Cloudera Search Architecture • Component deep dive • Security • Conclusion

  3. Why Search? • Hadoop for everyone • Typical case: • Ingest data to storage engine (HDFS, HBase, etc) • Process data (MapReduce, Hive, Impala) • Experts know MapReduce • Savvy people know SQL • Everyone knows Search!

  4. Why Search? An Integrated Part of the Hadoop System One pool of data One security framework One set of system resources One management interface

  5. Benefits of Search • Improved Big Data ROI • An interactive experience without technical knowledge • Single data set for multiple computing frameworks • Faster time to insight • Exploratory analysis, esp. unstructured data • Broad range of indexing options to accommodate needs • Cost efficiency • Single scalable platform; no incremental investment • No need for separate systems, storage

  6. What is Cloudera Search? • Full-text, interactive search with faceted navigation • Apache Solr integrated with CDH • Established, mature search with vibrant community • In production environments for years • Open Source • 100% Apache, 100% Solr • Standard SolrAPIs • Batch, near real-time, and on-demand indexing • Released 1.0 (GA) last week!

  7. Cloudera Search Components • HDFS/MR/Lucene/Solr/SolrCloud • Indexing • Near Real Time (NRT) indexing • Batch • ETL – Cloudera Morphlines • Querying

  8. Apache Hadoop • Apache HDFS • Distributed file system • High reliability • High throughput • Apache MapReduce • Parallel, distributed programming model • Allows processing of large datasets • Fault tolerant

  9. Apache Lucene • Full text search • Indexing • Query • Traditional inverted index • Batch and Incremental indexing • We are using version 4.4 in current release

  10. Apache Solr • Search service built using Lucene • Ships with Lucene (same TLP at Apache) • Provides XML/HTTP/JSON/Python/Ruby/… APIs • Indexing • Query • Administrative interface • Also rich web admin GUI via HTTP

  11. Apache SolrCloud • Provides distributed Search capability • Part of Solr (not a separate library/codebase) • Shards – provide scalability • partition index for size • replicate for query performance • Uses ZooKeeper for coordination • No split-brain issues • Simplifies operations

  12. SolrCloud Architecture • If sent to a replica, the document is forwarded to the leader for processing. • If the machine is a leader, SolrCloud determines which shard the document should go to, forwards the document the leader for that shard, indexes the document for this shard, and forwards the index notation to itself and any replicas.

  13. Distributed Search on Hadoop SolrCloud ZK Hue UI query Flume index Solr Custom UI query Solr index HBase query Custom App Solr index MR HDFS Hadoop Cluster

  14. Indexing • Near Real Time (NRT) • Flume • HBase Indexer • Batch (MR)

  15. Indexing • Near Real Time (NRT) • Flume • HBase Indexer • Batch (MR)

  16. Near Real Time Indexing with Flume Other Log File Log File • Solr and Flume • Data ingest at scale • Flexible extraction and mapping • Indexing at data ingest Flume Agent Flume Agent HDFS Indexer Indexer

  17. Apache Flume - MorphlineSolrSink • A Flume Source… • Receives/gathers events • A Flume Channel… • Carries the event – MemoryChannel or reliable FileChannel • A Flume Sink… • Sends the events on to the next location • Flume MorphlineSolrSink • Integrates Cloudera Morphlines library • ETL, more on that in a bit • Does batching • Results sent to Solr for indexing

  18. Indexing • Near Real Time (NRT) • Flume • HBase Indexer • Batch (MR)

  19. Near Real Time Indexing of Apache HBase planet-sized tabular data immediate access & updates fast & flexible informationdiscovery = + Search BIG DATA DATAMANAGEMENT HBase HBase Indexer(s) Solr server Solr server Trigger Solr server interactive load Solr server Solr server HDFS

  20. Lily HBase Indexer • Collaboration between NGData & Cloudera • NGData are creators of the Lily data management platform • Lily HBase Indexer • Service which acts as a HBase replication listener • HBase replication features, such as filtering, supported • Replication updates trigger indexing of updates (rows) • Integrates Cloudera Morphlines library for ETL of rows • AL2 licensed on githubhttps://github.com/ngdata

  21. Indexing • Near Real Time (NRT) • Flume • HBase Indexer • Batch (MR)

  22. Scalable Batch Indexing GOLIVE Solr server • Solr and MapReduce • Flexible, scalable batch indexing • Start serving new indices with no downtime • On-demand indexing, cost-efficient re-indexing Solr server Index shard Index shard Indexer HDFS Indexer Files Files

  23. MapReduce Indexer MapReduce Job with two parts 1) Scan HDFS for files to be indexed • Much like Unix “find” – see HADOOP-8989 • Output is NLineInputFormat’ed file 2) Mapper/Reducer indexing step • Mapper extracts content via Cloudera Morphlines • Reducer indexes documents via embedded Solr server • Originally based on SOLR-1301 • Many modifications to enable linear scalability

  24. MapReduce Indexer “golive” • Cloudera created this to bridge the gap between NRT (low latency, expensive) and Batch (high latency, cheap at scale) indexing • Results of MR indexing operation are immediately merged into a live SolrCloud serving cluster • No downtime for users • No NRT expense • Linear scale out to the size of your MR cluster

  25. Cloudera Morphlines • Open Source framework for simple ETL • Simplify ETL • Built-in commands and library support (Avro format, HadoopSequenceFiles, grok for syslog messages) • Configuration over coding • Standardize ETL • Ships as part Cloudera Developer Kit (CDK) • It’s a Java library • AL2 licensed on githubhttps://github.com/cloudera/cdk

  26. Cloudera Morphlines Architecture Morphlines can be embedded in any application… SolrCloud Solr Flume, MR Indexer, HBase indexer, etc... Or your application! Logs, tweets, social media, html, images, pdf, text…. Anything you want to index Solr Morphline Library Solr

  27. Extraction and Mapping syslog Flume Agent • Modeled after Unix pipelines • Simple and flexible data transformation • Reusable across multiple index workloads • Over time, extend and re-use across platform workloads Event Solr sink Record Morphline Library Command: readLine Record Command: grok Record Command: loadSolr Document Solr

  28. Morphline Example – syslog with grok morphlines : [  {    id : morphline1 importCommands : ["com.cloudera.**", "org.apache.solr.**"]    commands : [      { readLine {} }                          { grok { dictionaryFiles : [/tmp/grok-dictionaries]                                         expressions : {            message : """<%{POSINT:syslog_pri}>%{SYSLOGTIMESTAMP:syslog_timestamp} %{SYSLOGHOST:syslog_hostname} %{DATA:syslog_program}(?:\[%{POSINT:syslog_pid}\])?: %{GREEDYDATA:syslog_message}"""          }        }      }      { loadSolr {} }          ]  } ] Example Input <164>Feb  4 10:46:14 syslog sshd[607]: listening on 0.0.0.0 port 22 Output Record syslog_pri:164 syslog_timestamp:Feb  4 10:46:14 syslog_hostname:syslog syslog_program:sshd syslog_pid:607 syslog_message:listening on 0.0.0.0 port 22.

  29. Current Command Library • Integrate with and load into Apache Solr • Flexible log file analysis • Single-line record, multi-line records, CSV files • Regex based pattern matching and extraction • Integration with Avro • Integration with Apache Hadoop Sequence Files • Integration with SolrCell and all Apache Tika parsers • Auto-detection of MIME types from binary data using Apache Tika

  30. Current Command Library (cont) • Scripting support for dynamic java code • Operations on fields for assignment and comparison • Operations on fields with list and set semantics • if-then-else conditionals • Asmall rules engine (tryRules) • String and timestamp conversions • slf4j logging • Yammer metrics and counters • Decompression and unpacking of arbitrarily nested container file formats • Etc…

  31. Querying • Built-in solr web UI • Write your own • Hue

  32. Simple, Customizable Search Interface • Hue • Simple UI • Navigated, faceted drill down • Customizable display • Full text search, standard Solr API and query language

  33. Security • Upstream Solr doesn’t really deal with security • Search 1.0 supports kerberos authentication • Similar to Oozie / WebHDFS • Actively working on Index-level authorization using Apache Sentry • Future: more granular authorization

  34. Conclusion • Cloudera Search now Generally Available (1.0) • Free Download • Extensive documentation • Send your questions and feedback to search-user@cloudera.org • Take the Search online training • Cloudera Manager Standard (i.e. the free version) • Simple management of Search • Free Download • QuickStart VM also available!

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