Fast, Scalable Graph Processing: Apache Giraph on YARN
Upcoming SlideShare
Loading in...5
×
 

Fast, Scalable Graph Processing: Apache Giraph on YARN

on

  • 4,364 views

Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph ...

Apache Giraph performs offline, batch processing of very large graph datasets on top of a Hadoop cluster. Giraph replaces iterative MapReduce-style solutions with Bulk Synchronous Parallel graph processing using in-memory or disk-based data sets, loosely following the model of Google`s Pregel. Many recent advances have left Giraph more robust, efficient, fast, and able to accept a variety of I/O formats typical for graph data in and out of the Hadoop ecosystem. Giraph's recent port to a pure YARN platform offers increased performance, fine-grained resource control, and scalability that Giraph atop Hadoop MRv1 cannot, while paving the way for ports to other platforms like Apache Mesos. Come see whats on the roadmap for Giraph, what Giraph on YARN means, and how Giraph is leveraging the power of YARN to become a more robust, usable, and useful platform for processing Big Graph datasets.

Statistics

Views

Total Views
4,364
Views on SlideShare
4,357
Embed Views
7

Actions

Likes
14
Downloads
150
Comments
0

1 Embed 7

https://twitter.com 7

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Fast, Scalable Graph Processing: Apache Giraph on YARN Fast, Scalable Graph Processing: Apache Giraph on YARN Presentation Transcript

    • Fast, Scalable Graph Processing: Apache Giraph on YARN
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Hello, I'm Eli Reisman!
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Eli is... •  Apache Giraph Committer and PMC Member •  Apache Tajo Committer •  Wrote initial port of Giraph to YARN •  Collaborating with fellow Giraph committers on Giraph in Action book for Manning publishing
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Eli is... •  Only able to do all this with the support of:
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Eli is a software engineer at
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Etsy enables non-technical folks to sell handmade and vintage stuff: We have a great blog called Code As Craft:
    • Fast, Scalable Graph Processing: Apache Giraph on YARN ...but, enough about me, lets talk Giraph!
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Key Topics What is Apache Giraph? Why do I need it? Giraph + MapReduce Giraph + YARN Giraph Roadmap
    • Fast, Scalable Graph Processing: Apache Giraph on YARN What is Apache Giraph? Giraph is a framework for performing offline batch processing of semi-structured graph data on a massive scale. Giraph is loosely based upon Google's Pregel graph processing framework.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN What is Apache Giraph? Giraph performs iterative calculations on top of an existing Hadoop cluster.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN What is Apache Giraph? Giraph uses Apache Zookeeper to enforce atomic barrier waits and perform leader election. Done! Done! ...Still working...
    • Fast, Scalable Graph Processing: Apache Giraph on YARN What is Apache Giraph? Giraph benefits from a vibrant Apache community, and is under active development:
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Why do I need it? Giraph makes graph algorithms easy to reason about and implement by following the Bulk Synchronous Parallel (BSP) programming model. In BSP, all algorithms are implemented from the point of view of a single vertex in the input graph performing a single iteration of the computation.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Why do I need it? •  Giraph makes iterative data processing more practical for Hadoop users. •  Giraph can avoid costly disk and network operations that are mandatory in MR. •  No concept of message passing in MR.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Why do I need it? Each cycle of an iterative calculation on Hadoop means running a full MapReduce job.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Let's use simple PageRank as a quick example: http://en.wikipedia.org/wiki/PageRank 1.0 1.0 1.0
    • Fast, Scalable Graph Processing: Apache Giraph on YARN 1. All vertices start with same PageRank 1.0 1.0 1.0
    • Fast, Scalable Graph Processing: Apache Giraph on YARN 2. Each vertex distributes an equal portion of its PageRank to all neighbors: 0.5 0.5 1 1
    • Fast, Scalable Graph Processing: Apache Giraph on YARN 3. Each vertex sums incoming values times a weight factor and adds in small adjustment: 1/(# vertices in graph) (.5*.85) + (.15/3) (1.5*.85) + (.15/3) (1*.85) + (.15/3)
    • Fast, Scalable Graph Processing: Apache Giraph on YARN 4. This value becomes the vertices' PageRank for the next iteration .43 .21 .64
    • Fast, Scalable Graph Processing: Apache Giraph on YARN 5. Repeat until convergence: (change in PR per-iteration < epsilon)
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Vertices with more in-degrees converge to higher PageRank
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Put another way:
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 1. Load complete input graph from disk as [K= Vertex ID, V = out-edges and PR] Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 2. Emit all input records (full graph state), Emit [K = edgeTarget, V = share of PR] Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 3. Sort and Shuffle this entire mess! Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 4. Sum incoming PR shares for each vertex, update PR values in graph state records Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 5. Emit full graph state to disk... Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce 6. ...and start over! Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on MapReduce •  Awkward to reason about •  I/O bound despite simple core business logic Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on Giraph 1. Hadoop Mappers are "hijacked" to host Giraph master and worker tasks. Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on Giraph 2. Input graph is loaded once, maintaining code-data locality when possible. Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on Giraph 3. All iterations are performed on data in memory, optionally spilled to disk. Disk access is linear/ scan-based. Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN PageRank on Giraph 4. Output is written from the Mappers hosting the calculation, and the job run ends. Map Sort/Shuffle Reduce
    • Fast, Scalable Graph Processing: Apache Giraph on YARN This is all well and good, but must we manipulate Hadoop this way? ?
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph + MapReduce •  Heap and other resources are set once, globally, for all Mappers in the computation. •  No control of which cluster nodes host which tasks. •  No control over how Mappers are scheduled. •  Mapper and Reducer slots abstraction is meaningless for Giraph at best, an artificial limit at worst.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN YARN •  YARN (Yet Another Resource Negotiator) is Hadoop's next-gen job management platform. •  Powers MapReduce v2, but is a general purpose framework that is not tied to the MapReduce paradigm. •  Offers fine-grained control over each task's resource allocations and host placement for clients that need it.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN YARN Architecture
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph + YARN Its a natural fit!
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph + YARN •  Giraph has maintained compatibility with Hadoop since 0.1 release by executing via MapReduce interface. •  Giraph has featured a "pure YARN" build profile since 1.0 release. It supports Hadoop-2.0.3 and trunk. *Patches to add 2.0.4 and 2.0.5 support are in review :) •  Giraph's YARN component is easy to extend or use as a template to port other projects!
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph + YARN: Roadmap •  YARN Application Master allows for more natural and stable bootstrapping of Giraph jobs. •  Zookeeper management can find natural home in Application Master. •  Giraph on YARN can stop borrowing from Hadoop and have its own web interface.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph + YARN: Roadmap •  Variable per-task resource allocation opens up the possibility of Supertasks to manage graph supernodes. •  Ability to spawn or retire tasks per-iteration enables in- flight reassignment of data partitions. •  AppMaster managed utility tasks such as dedicated sub-aggregators for tree-like aggregation, or data pre- samplers.
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Giraph New Developments •  Decoupling of logic and graph data means tasks host computations that are pluggable per-iteration. •  Support for Giraph job scripting, starting with Jython. More to follow... •  New website, fresh docs, upcoming Manning book, and large, active community means Giraph has never been easier to use or contribute to!
    • Fast, Scalable Graph Processing: Apache Giraph on YARN Great! Where can I learn more? http://giraph.apache.org Mailing List: user@giraph.apache.org