Java @ Google JavaZone 2005 Knut Magne Risvik Google Inc. September 14, 2005.
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Transcript of Java @ Google JavaZone 2005 Knut Magne Risvik Google Inc. September 14, 2005.
Java @ Google
JavaZone 2005
Knut Magne RisvikGoogle Inc.September 14, 2005
Presentation Outline
• Background: Google’s mission and computing platform.
• GFS and MapReduce: Ebony and Ivory of our Infrastructure
• Java for computing: Coupling infrastructure and Java
• Java in Google products: apps and middle-tiers
• The Java expertise at Google: We host Java leadership.
• Giving it back: Google contributions to Java
• Closing notes and Q&A: Swags for good questions.
Google’s Mission
To organize the world’s information
and make it universally
accessible and useful
Explosive Computational Requirements
more queries
better results
more data
Every Google service sees continuing growth in computational needs
• More queries: More users, happier users
• More data: Bigger web, mailbox, blog, etc.
• Better results: Find the right information, and find it faster
A Simple Challenge For Our Computing Platform
1. Create world’s largest computing infrastructure
2. Make sure we can afford it
Need to drive efficiency of the computing infrastructure to unprecedented levels
Many Interesting Challenges
• Server design and architecture
• Power efficiency
• System software
• Large scale networking
• Performance tuning and optimization
• System management and repairs automation
Design Philosophy
Single-machine performance does not matter• The problems we are interested in are too large for any single system
• Can partition large problems, so throughput beats peak performance
Stuff Breaks• If you have one server, it may stay up three years (1,000 days)
• If you have 1,000 servers, expect to lose one a day
“Ultra-reliable” hardware makes programmers lazy• A reliable platform will still fail – software still needs to be fault-tolerant
• Fault-tolerant software beats fault-tolerant hardware
Why Use Commodity PCs?
• Single high-end 8-way Intel server:– IBM eserver xSeries 440– 8 2-GHz Xeon, 64 GB RAM, 8 TB of disk– $758,000
• Commodity machines: – Rack of 88 machines– 176 2-GHz Xeons, 176 GB RAM, ~7 TB of disk– $278,000
• 1/3X price, 22X CPU, 3X RAM, 1X disk
Sources: racksaver.com, TPC-C performance results, both from late 2002
When Ultra-reliable Machines Won’t Help…
Take-home lesson: Murphy was right
google.stanford.edu (circa 1997)
Lego Disk Case
google.com (1999)
Google Data Center (circa 2000)
google.com (new data center 2001)
google.com (3 days later)
When Servers Sleep… (2004)
Google Web Server
Spell checker
Ad Server
I0 I1 I2 IN
I0 I1 I2 IN
I0 I1 I2 IN
Replic
as …
…
Index shards
D0 D1 DM
D0 D1 DM
D0 D1 DM
Replic
as …
…Doc shards
query
Misc. servers
Index servers Doc servers
Elapsed time: 0.25s, machines involved: 1000+
Google Query Serving Infrastructure
Reliable Building Blocks
• Need to store data reliably
• Need to run jobs on pools of machines
• Need to make it easy to apply lots of computational resources to problems
In-house solutions:
• Storage: Google File System (GFS)
• Job scheduling: Global Work Queue (GWQ)
• MapReduce: simplified large-scale data processing
Client
Client
Misc. servers
Client
Replic
as
Masters
GFS Master
GFS Master
C0 C1
C2C5
Chunkserver 1
C0
C2
C5
Chunkserver N
C1
C3C5
Chunkserver 2
…
• Master manages metadata• Data transfers happen directly between clients/chunkservers• Files broken into chunks (typically 64 MB)• Chunks triplicated across three machines for safety
Google File System - GFS
GoogleFile API access to GFS
• GoogleFile. Public API with two roles:
– Creational class. Static methods to obtain InputStream, OutputStream and GoogleChannel on top of a Google file.
– File manipulation. A subset of the methods provided by the java.io.File class.
• GoogleInputStream. Implements the read method.
• GoogleOutputStream. Extends java.io.OutputStream, write method.
• GoogleChannel. This is a public class. It implements the ByteChannel interface and a subset of the methods in the FileChannel class. This class provides random access.
• GoogleFile.Stats.
• The JNI Layer is implemented by the class FileImpl and set of SWIG JNI wrappers generated during the build process.
GFS Usage at Google
• 30+ Clusters
• Clusters as large as 2000+ chunkservers
• Petabyte-sized filesystems
• 2000+ MB/s sustained read/write load
• All in the presence of HW failures
• More information can be found:
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung 19th ACM Symposium on Operating Systems Principles
(http://labs.google.com/papers/gfs.html)
MapReduce: Large Scale Data Processing
• Many tasks: Process lots of data to produce other data
• Want to use hundreds or thousands of CPUs, and it has to be easy
• MapReduce provides, for programs following a particular programming model:
– Automatic parallelization and distribution
– Fault-tolerance
– I/O scheduling
– Status and monitoring
Example: Word Frequencies in Web Pages
A typical exercise for a new engineer in his or her first week
• Have files with one document per record
• Specify a map function that takes a key/value pairkey = document namevalue = document text
• Output of map function is (potentially many) key/value pairs.In our case, output (word, “1”) once per word in the document
“document1”, “to be or not to be”
“to”, “1”“be”, “1”“or”, “1”…
Example continued: word frequencies in web pages
• MapReduce library gathers together all pairs with the same key
• The reduce function combines the values for a keyIn our case, compute the sum
• Output of reduce (usually 0 or 1 value) paired with key and saved
“be”, “2”“not”, “1”“or”, “1”“to”, “2”
key = “or”values = “1”
“1”
key = “be”values = “1”, “1”
“2”
key = “to”values = “1”, “1”
“2”
key = “not”values = “1”
“1”
Example: Pseudo-code
map(String input_key, String input_value): // input_key: document name // input_value: document contents for each word w in input_values: EmitIntermediate(w, "1");
Reduce(String key, Iterator intermediate_values): // key: a word, same for input and output // intermediate_values: a list of counts int result = 0; for each v in intermediate_values: result += ParseInt(v); Emit(AsString(result));
Total 80 lines of code
Typical Google Cluster
• 100s/1000s of 2-CPU x86 machines, 2-4 GB of memory
• Limited bisection bandwidth
• Storage: local IDE disks and Google File System (GFS)
• GFS running on the same machines provides reliable, replicated storage of input and output data
• Job scheduling system: jobs made up of tasks, scheduler assigns tasks to machines
Execution
GFS: Google File System
GFS: Google File System
Map task 1
map
k1:v k2:v
Map task 2
map
k1:v k3:v
Map task 3
map
k1:v k4:v
reduce
k1:v,v,v k3,v
Reduce task 1
reduce
k2:v k4,v
Reduce task 2
Shuffle and Sort
Optimizations
• Shuffle stage is pipelined with mapping
• Many more tasks than machines, for load balancing
• Locality: map tasks scheduled near the data they read
• Backup copies of map & reduce tasks (avoids stragglers)
• Compress intermediate data
• Re-execute tasks on machine failure
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
MapReduce status: MR_Indexer-beta6-large-2003_10_28_00_03
Using 1800 machines:
• MR_Grep scanned 1 terabyte in 100 seconds
• MR_Sort sorted 1 terabyte of 100 byte records in 14 minutes
Rewrote Google's production indexing system
• a sequence of 7, 10, 14, 17, 21, 24 MapReductions
• simpler
• more robust
• faster
• more scalable
Results
Usage in March 2005
Number of jobs 72,229
Average completion time 934 secs
Machine days used 358,528 days ≈ 1 millennium
Input data read 12,571 TB
Intermediate data 2,756 TB
Output data written 941 TB
Average worker machines 232
Average worker deaths per job 1.9
Average map tasks per job 3097
Average reduce tasks per job 144
Unique map implementations 309
Unique reduce implementations 235
Unique map/reduce combinations 411
Widely applicable at Google
– Implemented as a C++ library linked to user programs
– Java JNI interface similar to GFS API.
– Can read and write many different data types
Example uses:
web access log statsweb link-graph reversalinverted index constructionstatistical machine translation…
distributed grepdistributed sortterm-vector per hostdocument clusteringmachine learning...
Conclusion
• MapReduce has proven to be a useful abstraction
• Greatly simplifies large-scale computations at Google
• Fun to use: focus on problem, let library deal with messy details
MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay GhemawatOSDI'04: Sixth Symposium on Operating System Design and Implementation
(Search Google for “MapReduce”)
Java in Google Applications
AdWords FE - Millions of ads - Billions of transactions - Extreme rates
GMail middletier - UI and storage brokerage - Content searching, analysis - Tagging
Java expertise @ Google
• Joshua Bloch - Collections Framework, Java 5.0 language enhancements, java.math, Author of "Effective Java," Coauthor of "Java Puzzlers."
• Neal Gafter - Lead developer of javac, implementor of Java 5.0 language enhancements, "Coauthor of "Java Puzzlers."
• Robert Griesmer - Architect and technical lead of the HotSpot JVM.
• Doug Kramer - Javadoc architect, Java platform documentation lead.
• Tim Lindholm - Original member of the Java project, key contributor to the Java programming language, implementor of the classic JVM, coauthor of "The Java Viutual Machine Specification."
• Michael "madbot" McCloskey - Designer and implementer of java.util.regexp.
• Srdjan Mitrovic - Co-implementor of the HotSpot JVM.
• David Stoutamire - Technical lead for Java performance, designer and implementer of parallel garbage collection.
• Frank Yellin - Original member of the Java project, Co-implementor of classic JVM, KVM and CLDC, Coauthor of "The Java Viutual Machine specification."
Giving it back – JCP Expert Groups
• Executive Committe for J2SE/J2EE
• JSR 166X: Concurrency Utilities (continuing) http://www.jcp.org/en/jsr/detail?id=166
• JSR 199: Java Compiler API http://www.jcp.org/en/jsr/detail?id=199
• JSR 220: Enterprise JavaBeans 3. http://www.jcp.org/en/jsr/detail?id=220
• JSR 250: Common Annotations for the Java Platform http://www.jcp.org/en/jsr/detail?id=250
• JSR 260: Javadoc Tag Technology Update http://www.jcp.org/en/jsr/detail?id=260
• JSR 269: Pluggable Annotation Processing API http://www.jcp.org/en/jsr/detail?id=269
• JSR 270: J2SE 6.0 ("Mustang") Release Contents http://www.jcp.org/en/jsr/detail?id=270Google representative: gafter
• JSR 273: Design-Time API for JavaBeans JBDT http://www.jcp.org/en/jsr/detail?id=273
• JSR 274: The BeanShell Scripting Language http://www.jcp.org/en/jsr/detail?id=274
• JSR 277: Java Module System http://www.jcp.org/en/jsr/detail?id=277
Closing Notes
• Google = Computing infrastructure
• Java is becoming a first class citizen at Google
• Essential native interfaces being built
• API design extremely important at our scale, the Java expertise is driving general API work
• Google brings high-scale industrial experience into JCP expert groups.
Knut Magne RisvikGoogle Inc.September 14, 2005
Q&A