Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Spark Internals

Spark Version: 1.0.2
Doc Version: 1.0.2.0

Authors

Weibo/Twitter ID	Name	Contributions
@JerryLead	Lijie Xu	Author of the original Chinese version, and English version update
@juhanlol	Han JU	English version and update (Chapter 0, 1, 3, 4, and 7)
@invkrh	Hao Ren	English version and update (Chapter 2, 5, and 6)

Introduction

This series discuss the design and implementation of Apache Spark, with focuses on its design principles, execution mechanisms, system architecture and performance optimization. In addition, there's some comparisons with Hadoop MapReduce in terms of design and implementation. I'm reluctant to call this document a "code walkthrough", because the goal is not to analyze each piece of code in the project, but to understand the whole system in a systematic way (through analyzing the execution procedure of a Spark job, from its creation to completion).

There're many ways to discuss a computer system. Here, We've chosen a problem-driven approach. Firstly one concret problem is introduced, then it gets analyzed step by step. We'll start from a typical Spark example job and then discuss all the related important system modules. I believe that this approach is better than diving into each module right from the beginning.

The target audiences of this series are geeks who want to have a deeper understanding of Apache Spark as well as other distributed computing frameworks.

I'll try my best to keep this documentation up to date with Spark since it's a fast evolving project with an active community. The documentation's main version is in sync with Spark's version. The additional number at the end represents the documentation's update version.

For more academic oriented discussion, please check out Matei's PHD thesis and other related papers. You can also have a look at my blog (in Chinese) blog.

I haven't been writing such complete documentation for a while. Last time it was about three years ago when I'm studying Andrew Ng's ML course. I was really motivated at that time! This time I've spent 20+ days on this document, from the summer break till now (August 2014). Most of the time is spent on debugging, drawing diagrams and thinking how to put my ideas in the right way. I hope you find this series helpful.

Overview Overview of Apache Spark
Job logical plan Logical plan of a job (data dependency graph)
Job physical plan Physical plan
Shuffle details Shuffle process
Architecture Coordination of system modules in job execution
Cache and Checkpoint Cache and Checkpoint
Broadcast Broadcast feature
Job Scheduling TODO
Fault-tolerance TODO

Chinses Verison is at markdown/.

The documentation is written in markdown. The pdf version is also available here.

If you're under Max OS X, I recommand MacDown with a github theme for reading.

Gitbook (Chinese version)

Thanks @Yourtion for creating the gitbook version.

Online reading http://spark-internals.books.yourtion.com/

Downloads

Examples

I've created some examples to debug the system during the writing, they are avaible under SparkLearning/src/internals.

Acknowledgement

I appreciate the help from the following in providing solutions and ideas for some detailed issues:

@Andrew-Xia Participated in the discussion of BlockManager's implemetation's impact on broadcast(rdd).
@CrazyJVM Participated in the discussion of BlockManager's implementation.
@王联辉 Participated in the discussion of BlockManager's implementation.

Thanks to the following for complementing the document:

Weibo ID	Chapter	Content	Revision status
@OopsOutOfMemory	Overview	Relation between workers and executors and Summary on Spark Executor Driver's Resouce Management (in Chinese)	There's not yet a conclusion on this subject since its implementation is still changing, a link to the blog is added

Thanks to the following for finding errors:

Weibo Id	Chapter	Error/Issue	Revision status
@Joshuawangzj	Overview	When multiple applications are running, multiple Backend process will be created	Corrected, but need to be confirmed. No idea on how to control the number of Backend processes
@_cs_cm	Overview	Latest groupByKey() has removed the mapValues() operation, there's no MapValuesRDD generated	Fixed groupByKey() related diagrams and text
@染染生起	JobLogicalPlan	N:N relation in FullDepedency N:N is a NarrowDependency	Modified the description of NarrowDependency into 3 different cases with detaild explaination, clearer than the 2 cases explaination before
@zzl0	Fisrt four chapters	Lots of typos，such as "groupByKey has generated the 3 following RDDs"，should be 2. Check pull request。	All fixed
@左手牵右手TEL	Cache and Broadcast chapter	Lots of typos	All fixed
@cloud-fan	JobLogicalPlan	Some arrows in the Cogroup() diagram should be colored red	All fixed
@CrazyJvm	Shuffle details	Starting from Spark 1.1, the default value for spark.shuffle.file.buffer.kb is 32k, not 100k	All fixed

Special thanks to @明风Andy for his great support.

Special thanks to the rockers (including researchers, developers and users) who participate in the design, implementation and discussion of big data systems.

Name		Name	Last commit message	Last commit date
EnglishVersion		EnglishVersion
Graphs		Graphs
PNGfigures		PNGfigures
markdown		markdown
pdf		pdf
.DS_Store		.DS_Store
.bookignore		.bookignore
.gitignore		.gitignore
Introduction.md		Introduction.md
SUMMARY.md		SUMMARY.md
book.json		book.json
readme.md		readme.md

Name		Name	Last commit message	Last commit date
EnglishVersion		EnglishVersion
Graphs		Graphs
PNGfigures		PNGfigures
markdown		markdown
pdf		pdf
.DS_Store		.DS_Store
.bookignore		.bookignore
.gitignore		.gitignore
Introduction.md		Introduction.md
SUMMARY.md		SUMMARY.md
book.json		book.json
readme.md		readme.md

Name		Name	Last commit message	Last commit date
EnglishVersion		EnglishVersion
Graphs		Graphs
PNGfigures		PNGfigures
markdown		markdown
pdf		pdf
.DS_Store		.DS_Store
.bookignore		.bookignore
.gitignore		.gitignore
Introduction.md		Introduction.md
SUMMARY.md		SUMMARY.md
book.json		book.json
readme.md		readme.md

Name		Name	Last commit message	Last commit date
EnglishVersion		EnglishVersion
Graphs		Graphs
PNGfigures		PNGfigures
markdown		markdown
pdf		pdf
.DS_Store		.DS_Store
.bookignore		.bookignore
.gitignore		.gitignore
Introduction.md		Introduction.md
SUMMARY.md		SUMMARY.md
book.json		book.json
readme.md		readme.md

Name		Name	Last commit message	Last commit date
EnglishVersion		EnglishVersion
Graphs		Graphs
PNGfigures		PNGfigures
markdown		markdown
pdf		pdf
.DS_Store		.DS_Store
.bookignore		.bookignore
.gitignore		.gitignore
Introduction.md		Introduction.md
SUMMARY.md		SUMMARY.md
book.json		book.json
readme.md		readme.md

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Examples

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About

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Spark Internals

Authors

Introduction

Contents

Gitbook (Chinese version)

Examples

Acknowledgement

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Latest commit

History

Folders and files

Repository files navigation

Spark Internals

Authors

Introduction

Contents

Gitbook (Chinese version)

Examples

Acknowledgement

About

Resources

Stars

Watchers

Forks

Releases

Packages

Contributors

Latest commit

History

Folders and files

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