2013: year of real-time access to Big Data?

Geoffrey Hendrey@geoffhendrey

@vertascale

Agenda

• Hadoop MapReduce basics• Hadoop stack & data formats• File access times and mechanics• Key-based indexing systems

(HBase)• MapReduce, Hive/Pig• MPP approaches & alternatives

A very bad* diagram

*this diagram makes it appear that data flows through the master node.

A better picture

Job Configuration

http://wiki.apache.org/hadoop/WordCount

Map and Reduce Java Code

Reduce

Reducer Group Iterators

• Reducer groups values together by key• Your code will iterate over the values, emit reduced

result

• Hadoop reducer value iterators return THE SAME OBJECT each next(). Object is “reused” to reduce garbage collection load

• Beware of “reused” objects (this is a VERY common cause of long and confusing debugs)

• Cause for concern: you are emitting an object with non-primitive values. STALE “reused object” state from previous value.

Bear:[1,1] Bear:2

Hadoop Writables

• Values in Hadoop are transmitted (shuffled, emitted) in a binary format

• Hadoop includes primitive types: IntWritable, Text, LongWritable, etc

• You must implement Writable interface for custom objects

public void write(DataOutput d) throws IOException { d.writeUTF(this.string); d.writeByte(this.column);} public void readFields(DataInput di) throws IOException { this.string = di.readUTF(); this.column = di.readByte();}

Hadoop Keys (WritableComparable)• Be very careful to implement equals and

hashcode consistently with compareTo()• compareTo() will control the sort order of keys

arriving in reducer• Hadoop includes ability to write custom partitioner

public int getPartition(Document doc, Text v, int numReducers) { return doc.getDocId()%numReducers;}

Typical Hadoop File Formats

Hadoop Stack Review

Distributed File System

HDFS performance characteristics• HDFS was designed for high throughput,

not low seek latency• best-case configurations have shown HDFS

to perform 92K/s random reads [http://hadoopblog.blogspot.com/]

• Personal experience: HDFS very robust. Fault tolerance is “real”. I’ve unplugged machines and never lost data.

Motivation for Real-time Hadoop• Big Data is more opaque than small data– Spreadsheets choke– BI tools can’t scale– Small samples often fail to replicate issues

• Engineers, data scientists, analysts need:– Faster “time to answer” on Big Data– Rapid “find, quantify, extract”

• Solve “I don’t know what I don’t know”• MapReduce jobs are hard to debug

Survey or real-time capabilities• Real-time, in-situ, self-service is the

“Holy Grail” for the business analyst• spectrum of real-time capabilities

exists on Hadoop

Easy Hard

Available in Hadoop Proprietary

HDFS HBase Drill

Real-time spectrum on HadoopUse Case Support Real-time

Seek to a particular byte in a distributed file

HDFS YES

Seek to a particular value in a distributed file, by key (1-dimensional indexing)

HBase YES

Answer complex questions expressible in code (e.g. matching users to music albums). Data science.

MapReduce (Hive, Pig)

NO

Ad-hoc query for scattered records given simple constraints (“field[4]==“music” && field[9]==“dvd”)

MPP Architectures

YES

Hadoop Underpinned By HDFS

• Hadoop Distributed File System (HDFS) • inspired by Google FileSystem (GFS)• underpins every piece of data in “Hadoop”• Hadoop FileSystem API is pluggable• HDFS can be replaced with other suitable

distributed filesystem– S3– kosmos– etc

Amazon S3

MapFile for real-time access?

– Index file must be loaded by client (slow)

– Index file must fit in RAM of client by default

– scan an average of 50% of the sampling interval

– Large records make scanning intolerable– not a viable “real world” solution for

random access

Apache HBase

• Clone of Google’s Big Table.• Key-based access mechanism• Designed to hold billions of rows• “Tables” stored in HDFS• Supports MapReduce over tables,

into tables• Requires you to think hard, and

commit to a key design.

HBase Architecture

HBase random read performancehttp://hstack.org/hbase-performance-testing/• 7 servers, each with• 8 cores• 32GB DDR3 and • 24 x 146GB SAS 2.0 10K RPM disks.

• Hbase table• 3 billion records,• 6600 regions.• data size is between 128-256 bytes per row,

spread in 1 to 5 columns.

Zoomed-in “Get” time histogram

http://hstack.org/hbase-performance-testing/

MapReduce

• “MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers”-wikipedia

• MapReduce is strongly tied to HDFS in Hadoop.

• Systems built on HDFS (i.e. HBase) leverage this common foundation for integration with the MR paradigm

MapReduce and Data Science

• Many complex algorithms can be expressed in the MapReduce paradigm– NLP– Graph processing– Image codecs

• The more complex the algorithm, the more Map and Reduce processes become complex programs in their own right.

• Often cascade multiple MR jobs in succession

Is MapReduce real-time?

• MapReduce on Hadoop has certain latencies that are hard to improve – Copy– Shuffle, sort– Iterate

• time-dependent on the both the size of the input data and the number of processors available

• In a nutshell, it’s a “batch process” and isn’t “real-time”

Hive and Pig

• Run on top of MapReduce• Provide “Table” metaphor familiar to SQL

users• Provide SQL-like (or actually same) syntax• Store a “schema” in a database, mapping

tables to HDFS files• Translate “queries” to MapReduce jobs• No more real-time than MapReduce

MPP Architectures

• Massively Parallel Processing• Lots of machines, so also lots of memoryExamples:• Spark – general purpose data science

framework sort of like real-time MapReduce for data science

• Dremel – columnar approach, geared toward answering SQL-like aggregations and BI-style questions

Spark

• Originally designed for iterative machine learning problems at Berkeley

• MapReduce does not do a great job on iterative workloads

• Spark makes more explicit use of memory caches than Hadoop

• Spark can load data from any Hadoop input source

Effect of Memory Caching in Spark

Is Spark Real-time?

• If data fits in memory, execution time for most algorithms still depends on– amount of data to be processed– number of processors

• So, it still “depends”• …but definitely more focused on fast time-

to-answer• Interactive scala and java shells

Dremel MPP architecture

• MPP architecture for ad-hoc query on nested data

• Apache Drill is an OS clone of Dremel• Dremel originally developed at Google• Features “in situ” data analysis• “Dremel is not intended as a replacement

for MR and is often used in conjunction with it to analyze outputs of MR pipelines or rapidly prototype larger computations.” -Dremel: Interactive Analysis of WebScaleDatasets

In Situ Analysis

• Moving Big Data is a nightmare• In situ: ability to access data in

place – In HDFS– In Big Table

Uses For Dremel At Google

• Analysis of crawled web documents.• Tracking install data for applications on Android

Market.• Crash reporting for Google products.• OCR results from Google Books.• Spam analysis.• Debugging of map tiles on Google Maps.• Tablet migrations in managed Bigtable instances.• Results of tests run on Google’s distributed build

system.• Etc, etc.

Why so many uses for Dremel?

• On any Big Data problem or application, dev team faces these problems:– “I don’t know what I don’t know” about data– Debugging often requires finding and

correlating specific needles in the haystack– Support and marketing often require

segmentation analysis (identify and characterize wide swaths of data)

• Every developer/analyst wants– Faster time to answer– Fewer trips around the mulberry bush

Column Oriented Approach

Dremel MPP query execution tree

Is Dremel real-time?

Alternative approaches?

• Both MapReduce and MPP query architectures take “throw hardware at the problem” approach.

• Alternatives?– Use MapReduce to build distributed indexes on

data– Combine columnar storage and inverted

indexes to create columnar inverted indexes– Aim for the sweet spot for data scientist and

engineer: Ad-hoc queries with results returned in seconds on a single processing node.

Contact Info

Email: geoff@vertascale.com

Twitter: @geoffhendrey @vertascale

www: http://vertascale.com

references

• http://www.ebaytechblog.com/2010/10/29/hadoop-the-power-of-the-elephant/

• http://bradhedlund.com/2011/09/10/understanding-hadoop-clusters-and-the-network/

• http://yoyoclouds.wordpress.com/tag/hdfs/• http://blog.cloudera.com/blog/2009/02/the-small-files-p

roblem/• http://hadoopblog.blogspot.com/• http://lunarium.info/arc/images/Hbase.gif• http://www.zdnet.com/i/story/60/01/073451/zdnet-ama

zon-s3_growth_2012_q1_1.png• http://hstack.org/hbase-performance-testing/• http://en.wikipedia.org/wiki/File:Mapreduce_Overview.s

vg• http://www.rabidgremlin.com/data20/MapReduceWord

CountOverview1.png• Dremel: Interactive Analysis of WebScale Datasets