solr + hadoop: interactive search for hadoop
DESCRIPTION
Solr + Hadoop: Interactive Search for HadoopTRANSCRIPT
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Solr + Hadoop: Interactive Search for Hadoop
Gregory Chanan (gchanan AT cloudera.com)OC Big Data Meetup 07/16/14
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive• Security• Conclusion
Agenda
• 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
• 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 Solr APIs
• Batch, near real-time, and on-demand indexing• Available for CDH4 and CDH5
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive• Security• Conclusion
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 library• Indexing• Querying
• 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
• Updates automatically sent to the correct shard
• Replicas handle queries, forward updates to the leader
• Leader indexes the document for the shard, and forwards the index notation to itself and any replicas.
SolrCloud Architecture
Visual representation via admin UI
Distributed Search on Hadoop
FlumeHue UI
Custom UI
Custom App
Solr
Solr
Solr
SolrCloudquery
query
query
index
Hadoop Cluster
MR
HDFS
index
HBaseindex
ZK
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive
• Indexing• ETL - morphlines• Querying
• Security• Conclusion
Indexing
• Near Real Time (NRT)• Flume• HBase Indexer
• Batch• MapReduceIndexerTool• HBaseBatchIndexer
Near Real Time Indexing with Flume
Log File Solr and Flume• Data ingest at scale• Flexible extraction and
mapping• Indexing at data ingest
HDFS
Flume Agent
Indexer
OtherLog File
Flume Agent
Indexer
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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
Indexing
• Near Real Time (NRT)• Flume• HBase Indexer
• Batch• MapReduceIndexerTool• HBaseBatchIndexer
Near Real Time Indexing of Apache HBase
HDFS
HBase
inte
racti
ve lo
ad
HBase Indexer(s)
Repl
icati
on Solr serverSolr serverSolr serverSolr serverSolr server
Sear
ch+ =planet-sized tabular dataimmediate access & updatesfast & flexible informationdiscovery
B I G DATA D ATA M A N A G E M E N T
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 https://github.com/ngdata
Indexing
• Near Real Time (NRT)• Flume• HBase Indexer
• Batch• MapReduceIndexerTool• HBaseBatchIndexer
Scalable Batch Indexing
Index shard
Files
Index shard
Indexer
Files
Solr server
Indexer
Solr server
GOLIVE
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HDFS
Solr and MapReduce• Flexible, scalable batch
indexing• Start serving new indices
with no downtime• On-demand indexing, cost-
efficient re-indexing
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
Indexing
• Near Real Time (NRT)• Flume• HBase Indexer
• Batch• MapReduceIndexerTool• HBaseBatchIndexer
HBase + MapReduce
• Run MapReduce job over HBase tables• Same architecture as running over HDFS• Similar to HBase’s CopyTable• Support for go-live
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive
• Indexing• ETL - morphlines• Querying
• Security• Conclusion
Cloudera Morphlines
• Open Source framework for simple ETL• Simplify ETL
• Built-in commands and library support (Avro format, Hadoop SequenceFiles, grok for syslog messages)
• Configuration over coding
• Standardize ETL• Ships as part of Kite SDK, formerly Cloudera
Developer Kit (CDK)• It’s a Java library• AL2 licensed on github https://github.com/kite-sdk
Cloudera Morphlines Architecture
Solr
Solr
Solr
SolrCloud
Logs, tweets, social media, html,
images, pdf, text….
Anything you want to index
Flume, MR Indexer, HBase indexer, etc... Or your application!
Morphline Library
Morphlines can be embedded in any application…
Extraction and Mapping
• Modeled after Unix pipelines (records instead of lines)
• Simple and flexible data transformation
• Reusable across multiple index workloads
• Over time, extend and re-use across platform workloads
syslog Flume Agent
Solr sink
Command: readLine
Command: grok
Command: loadSolr
Solr
Event
Record
Record
Record
Document
Mor
phlin
e Li
brar
y
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 22Output Recordsyslog_pri:164syslog_timestamp:Feb 4 10:46:14syslog_hostname:syslogsyslog_program:sshdsyslog_pid:607syslog_message:listening on 0.0.0.0 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 • A small rules engine (tryRules)• String and timestamp conversions • slf4j logging• Yammer metrics and counters • Decompression and unpacking of arbitrarily nested container
file formats• Etc…
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive
• Indexing• ETL - morphlines• Querying
• Security• Conclusion
Querying
• 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
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive• Security• Conclusion
Security
• Upstream Solr doesn’t deal with security• Cloudera Search supports kerberos authentication
• Similar to Oozie / WebHDFS• Collection-Level Authorization via Apache Sentry• Document-Level Authorization via Apache Sentry
(new in CDH5.1)
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive
• Indexing• ETL - morphlines• Querying
• Security• Collection-Level Authorization• Document-Level Authorization
• Conclusion
Collection-Level Authorization
• Sentry supports role-based granting of privileges• each role can be granted QUERY, UPDATE, and/or
administrative privileges on an index (collection)• Privileges stored in a “policy file” on HDFS
Policy File
[groups]# Assigns each Hadoop group to its set of rolesdev_ops = engineer_role, ops_role[roles]# Assigns each role to its set of privilegesengineer_role = collection = source_code->action=Query, collection = source_code- > action=Updateops_role = collection = hbase_logs->action=Query
Integrating Sentry and Solr
• Solr Request Handlers:
• Specified per collection in solrconfig.xml:
• Request to: http://localhost:8983/solr/collection1/select Is dispatched to an instance of solr.SearchHandler
Sentry Request Handlers
• Sentry ships with its own version of solrconfig.xml with secure handlers, called solrconfig.xml.secure
• Use a SearchComponent to implement the checking• Update Requests handled in a similar way
Agenda
• Big Data and Search – setting the stage• Cloudera Search Architecture• Component Deep Dive
• Indexing• ETL - morphlines• Querying
• Security• Collection-Level Authorization• Document-Level Authorization
• Conclusion
Document-level authorization Motivation
• Index-level authorization useful when access control requirements for documents are homogeneous
• Security requirements may require restricting access to a subset of documents
Document-level authorization Motivation
• Consider “Confidential” and “Secret” documents. How to store with only index-level authorization?
• Pushes complexity to application. Doc-level authorization designed to solve this problem
Document-level authorization model
• Instead of storing in HDFS Policy File:[groups]# Assigns each Hadoop group to its set of rolesdev_ops = engineer_role, ops_role[roles]# Assigns each role to its set of privilegesengineer_role = collection = source_code->action=Query, collection = source_code- > action=Updateops_role = collection = hbase_logs->action=Query
• Store authorization tokens in each document• Many more documents than collections; doesn’t scale to
store document-level info in Policy File• Can use Solr’s built-in filtering capabilities to restrict access
Document-level authorization model
• A configurable token field stores the authorization tokens• The authorization tokens are Sentry roles, i.e. “ops_role”
[roles]ops_role = collection = hbase_logs->action=Query
• Represents the roles that are allowed to view the document. To view a document, the querying user must belong to at least one role whose token is stored in the token field
• Can modify document permissions without restarting Solr• Can modify role memberships without reindexing
Document-level authorization impl
• Intercepts the request via a SearchComponent• SearchComponent adds an “fq” or FilterQuery
• Filter out all documents that don’t have “role1” or “role2” in authField
• Multiple “fq”s work as intersection, so malicious user can’t avoid by injection his own fq
• Filters are cached, so only construction expense once• Note: does not supersede index-level authorization
Document-level authorization config
• Configuration via solrconfig.xml.secure (per collection):
<!-- Set to true to enabled document-level authorization --> <bool name="enabled">false</bool> <!-- Field where the auth tokens are stored in the document --> <str name="sentryAuthField">sentry_auth</str> <!-- Auth token defined to allow any role to access the document. Uncomment to enable. --> <!--<str name="allRolesToken">*</str>-->• For backwards compatibility, not enabled• No tokens = no access. To allow all users to access a document,
use the allRolesToken. Useful for getting started
Conclusion
• Cloudera Search• 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!