Hadoop & Cloud Storage: Object Store Integration in Production

1 © Hortonworks Inc. 2011 – 2016. All Rights
Reserved
Hadoop & Cloud Storage:
Object Store Integration in
Production
Chris Nauroth
Rajesh Balamohan
Hadoop Summit 2016

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About Us
Rajesh Balamohan, rbalamohan@hortonworks.com, Twitter: @rajeshbalamohan
– Apache Tez Committer, PMC Member
– Mainly working on performance in Tez
– Have been using Hadoop since 2009
Chris Nauroth, cnauroth@hortonworks.com, Twitter: @cnauroth
– Apache Hadoop committer, PMC member, and Apache Software Foundation member
– Working on HDFS and alternative file systems such as WASB and S3A
– Hadoop user since 2010
Steve Loughran, stevel@hortonworks.com, Twitter: @steveloughran
– Apache Hadoop committer, PMC member, and Apache Software Foundation member
– Hadoop deployment since 2008, especially Cloud integration, Filesystem Spec author.
– Working on: Apache Slider, Spark+cloud integration, Hadoop + Cloud

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Agenda
⬢ Hadoop/Cloud Storage Integration Use Cases
⬢ Hadoop-compatible File System Architecture
⬢ Recent Enhancements in S3A FileSystem Connector
⬢ Hive Access Patterns
⬢ Performance Improvements and TPC-DS Benchmarks with Hive-TestBench
⬢ Next Steps for S3A and other Object Stores
⬢ Q & A

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Why Hadoop in the Cloud?

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Hadoop Cloud Storage Utilization Evolution
HDFS
Application
HDFS
Application
GoalEvolution towards cloud storage as the primary Data Lake
Input Output
Backup Restore
Input
Output
Copy
HDFS
Application
Input
Output
tmp

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What is the Problem?
Cloud Object Stores designed for
⬢ Scale
⬢ Cost
⬢ Geographic Distribution
⬢ Availability
⬢ Cloud app writers often modify apps to deal with cloud storage semantics and limitations
Challenges - Hadoop apps should work on HDFS or Cloud Storage transparently
⬢ Eventual consistency
⬢ Performance - separated from compute
⬢ Cloud Storage not designed for file-like access patterns
⬢ Limitations in APIs (e.g. rename)

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Goal and Approach
Goals
⬢ Integrate with unique functionality of each cloud
⬢ Optimize each cloud’s object store connector
⬢ Optimize upper layers for cloud object stores
Overall Approach
⬢ Consistency in face of eventual consistency (use a secondary metadata store)
⬢ Performance in the connector (e.g. lazy seek)
⬢ Upper layer improvements (Hive, ORC, Tez, etc.)

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Hadoop-compatible File System Architecture

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Hadoop-compatible File System Architecture
⬢ Applications
– File system interactions coded to file system-agnostic abstraction layer.
• FileSystem class - traditional API
• FileContext/AbstractFileSystem classes - newer API providing split between client API and provider API
– Can be retargeted to a different file system by configuration changes (not code changes).
• Caveat: Different FileSystem implementations may offer limited feature set.
• Example: Only HDFS and WASB can run HBase.
⬢ File System Abstraction Layer
– Defines interface of common file system operations: create, open, rename, etc.
– Supports additional mix-in interfaces to indicate implementation of optional features.
– Semantics of each operation documented in formal specification, derived from HDFS behavior.
⬢ File System Implementation Layer
– Each file system provides a set of concrete classes implementing the interface.
– A set of common file system contract tests execute against each implementation to prove its adherence to specified
semantics.

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© Hortonworks Inc. 2011 – 2016. All Rights
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Cloud Storage Connectors
Azure WASB ● Strongly consistent
● Good performance
● Well-tested on applications (incl. HBase)
ADL ● Strongly consistent
● Tuned for big data analytics workloads
Amazon Web Services S3A ● Eventually consistent - consistency work in
progress by Hortonworks
● Performance improvements in progress
● Active development in Apache
EMRFS ● Proprietary connector used in EMR
● Optional strong consistency for a cost
Google Cloud Platform GCS ● Multiple configurable consistency policies
● Currently Google open source
● Good performance
● Work under way for contribution to Apache

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Case Study: S3A Functionality and
Performance

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Authentication
⬢ Basic
– AWS Access Key ID and Secret Access Key in Hadoop Configuration Files
– Hadoop Credential Provider API to avoid using world-readable configuration files
⬢ EC2 Metadata
– Reads credentials published by AWS directly into EC2 VM instances
– More secure, because external distribution of secrets not required
⬢ AWS Environment Variables
– Less secure, but potentially easier integration for some applications
⬢ Session Credentials
– Temporary security credentials issued by Amazon Security Token Service
– Fixed lifetime reduces impact of credential leak
⬢ Anonymous Login
– Easy read-only access to public buckets for early prototyping

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Encryption
⬢ S3 Server-Side Encryption
– Encryption of data at rest at S3
– Supports the SSE-S3 option: each object encrypted by a unique key using AES-256 cipher
– Now covered in S3A automated test suites
– Support for additional options under development (SSE-KMS and SSE-C)

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Supportability
⬢ Documentation
– Backfill missing documentation, and include documentation in new enhancements
– To be published to hadoop.apache.org with Apache Hadoop 2.8.0 release
– Meanwhile, raw content visible on GitHub:
• https://github.com/apache/hadoop/blob/branch-2.8/hadoop-tools/hadoop-
aws/src/site/markdown/tools/hadoop-aws/index.md
⬢ Error Reporting
– Identify common user errors and provide more descriptive error messages
– S3 HTTP error codes examined and translated to specific error types
⬢ Instrumentation
– Internal metrics covering a wide range of metadata and data operations
– Already proven helpful in flagging a potential performance regression in a patch

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Performance Improvements
⬢ Lazy Seek
– Earlier implementation
• Reopened file in every seek call; Aborted connection in every reopen
• Positional Read was expensive (seek, read, seek)
– Current implementation
• Seek is a no-op call
• Performs real seek on need basis
⬢ Connection Abort Problem
– Backward seeks caused connection aborts
– Recent modifications to S3AFileSystem fixes these and added support for sequential reads
and random reads
• fs.s3a.experimental.input.fadvise

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Hive Access Patterns
⬢ ETL and Admin Activities
– Bringing in dataset / Creating Tables
– Cleansing / Transforming Data
– Analyze Tables, Compute Column Statistics
– MSCK to fix partition related information
⬢ Read
– Running Queries
⬢ Write
– Store Output

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Hive - MSCK Improvements
⬢ MSCK helps in fixing metastore for partitioned dataset
– Scan table path to identify missing partitions (expensive in S3)

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Hive - Analyze Column Statistics Improvements
⬢ Hive needs statistics to run queries efficiently
– Gathering table and column statistics can be expensive in partitioned datasets

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Performance Considerations When Running Hive Queries
⬢ Splits Generation
– File formats like ORC provides threadpool in split generation
⬢ ORC Footer Cache
– hive.orc.cache.stripe.details.size > 0
– Caches footer details; Helps in reducing data reads during split generation
⬢ Reduce S3A reads in Task side
– hive.orc.splits.include.file.footer=true
– Sends ORC footer information in splits payload.
– Helps reducing the amount of data read in task side.

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Performance Considerations When Running Hive Queries
⬢ Tez Splits Grouping
– Hive uses Tez as its default execution engine
– Tez groups splits based on min/max group setting, location details and so on
– S3A always provides “localhost” as its block location information
– When all splits-length falls below min group setting, Tez aggressively groups them into single
split. This causes issues with S3A as single task ends up doing sequential operations.
– Fixed in recent releases
⬢ Container Launches
– S3A always provides “localhost” for block locations.
– Good to set “yarn.scheduler.capacity.node-locality-delay=0”

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Hive-TestBench Benchmark Results
⬢ Hive-TestBench has subset of queries from TPC-DS (https://github.com/hortonworks/hive-testbench)
⬢ m4x4x large - 5 nodes
⬢ TPC-DS @ 200 GB Scale in S3
⬢ “HDP 2.3 + S3 in cloud” vs “HDP 2.4 + S3 in cloud”
– Average speedup 2.5x
– Queries like 15,17, 25, 73,75 etc did not run in HDP 2.3 (throws AWS timeout exceptions)

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Hive-TestBench Benchmark Results - LLAP
⬢ LLAP DAG runtime comparison with Hive
⬢ Reduces the amount of data to be read from S3 significantly; Improves runtime.

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Best Practices
⬢ Tune multipart settings
– fs.s3a.multipart.threshold (default: Integer.MAX_VALUE)
– fs.s3a.multipart.size (default: 100 MB)
– fs.s3a.connection.timeout (default: 200 seconds)
⬢ Disable node locality delay in YARN
– Set “yarn.scheduler.capacity.node-locality-delay=0” to avoid delays in container launches
⬢ Disable Storage Based authorization in Hive
– hive.security.metastore.authorization.manager=org.apache.hadoop.hive.ql.security.authorization.DefaultHiveMetas
toreAuthorizationProvider
– hive.metastore.pre.event.listeners= (set to empty value)
⬢ Tune ORC threads for reducing split generation times
– hive.orc.compute.splits.num.threads (default 10)

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Next Steps for S3A and other Object Stores
⬢ S3A Phase III
– https://issues.apache.org/jira/browse/HADOOP-13204
⬢ Output Committers
– Logical commit operation decoupled from rename (non-atomic and costly in object stores)
⬢ Object Store Abstraction Layer
– Avoid impedance mismatch with FileSystem API
– Provide specific APIs for better integration with object stores: saving, listing, copying
⬢ Ongoing Performance Improvement
– Less chatty call pattern for object listings
– Metadata caching to mask latency of remote object store calls
⬢ Consistency

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Summary
⬢ Evolution towards cloud storage
⬢ Hadoop-compatible File System Architecture fosters integration with cloud storage
⬢ Integration with multiple cloud providers available: Azure, AWS, Google
⬢ Recent enhancements in S3A
⬢ Hive usage and TPC-DS benchmarks show significant S3A performance
improvements
⬢ More coming soon for S3A and other object stores

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Q & A
Thank You!

Hadoop & Cloud Storage: Object Store Integration in Production

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