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Adding Search to the Hadoop Ecosystem PowerPoint PPT Presentation

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Adding Search to the Hadoop Ecosystem. Gregory Chanan ( gchanan AT 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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  • Adding Search to the

  • Hadoop Ecosystem

Gregory Chanan (gchanan AT

LA HUG Sept 2013


  • Big Data and Search – setting the stage

  • Cloudera Search Architecture

  • Component deep dive

  • Security

  • Conclusion

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!

Why Search?

An Integrated Part of the Hadoop System

One pool of data

One security framework

One set of system resources

One management interface

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

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!

Cloudera Search Components

  • HDFS/MR/Lucene/Solr/SolrCloud

  • Indexing

    • Near Real Time (NRT) indexing

    • Batch

  • ETL – Cloudera Morphlines

  • Querying

Apache Hadoop

  • Apache HDFS

    • Distributed file system

    • High reliability

    • High throughput

  • Apache MapReduce

    • Parallel, distributed programming model

    • Allows processing of large datasets

    • Fault tolerant

Apache Lucene

  • Full text search

    • Indexing

    • Query

  • Traditional inverted index

  • Batch and Incremental indexing

  • We are using version 4.4 in current release

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

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

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.

Distributed Search on Hadoop



Hue UI





Custom UI






Custom App





Hadoop Cluster


  • Near Real Time (NRT)

    • Flume

    • HBase Indexer

  • Batch (MR)


  • Near Real Time (NRT)

    • Flume

    • HBase Indexer

  • Batch (MR)

Near Real Time Indexing with Flume


Log File

Log File

  • Solr and Flume

  • Data ingest at scale

  • Flexible extraction and mapping

  • Indexing at data ingest

Flume Agent

Flume Agent




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


  • Near Real Time (NRT)

    • Flume

    • HBase Indexer

  • Batch (MR)

Near Real Time Indexing of Apache HBase

planet-sized tabular data

immediate access & updates

fast & flexible informationdiscovery






HBase Indexer(s)

Solr server

Solr server


Solr server

interactive load

Solr server

Solr server


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 github


  • Near Real Time (NRT)

    • Flume

    • HBase Indexer

  • Batch (MR)

Scalable Batch Indexing


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






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

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

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 github

  • Cloudera Morphlines Architecture

    Morphlines can be embedded in any application…



    Flume, MR Indexer, HBase indexer, etc...

    Or your application!

    Logs, tweets, social media, html, images, pdf, text….

    Anything you want to index


    Morphline Library


    Extraction and Mapping


    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


    Solr sink


    Morphline Library

    Command: readLine


    Command: grok


    Command: loadSolr



    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 port 22

    Output Record


    syslog_timestamp:Feb  4 10:46:14




    syslog_message:listening on port 22.

    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

    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…


    • Built-in solr web UI

    • Write your own

    • Hue

    Simple, Customizable Search Interface

    • Hue

    • Simple UI

    • Navigated, faceted drill down

    • Customizable display

    • Full text search, standard Solr API and query language


    • 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


    • Cloudera Search now Generally Available (1.0)

      • Free Download

      • Extensive documentation

      • Send your questions and feedback to [email protected]

      • 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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