Building Scalable Aggregation Systems
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This document summarizes aggregation systems for big data. It discusses how aggregations turn raw data into condensed, human-understandable information through techniques like time series, top-k, cardinality, and quantiles. It also covers abstraction concepts like monoids that allow different data types to be aggregated. Finally, it outlines common components of online incremental aggregation systems and provides examples like Twitter's Summingbird and Google's Mesa that compute aggregations incrementally in scalable key-value stores.
![Many Monoid Implementations
Already Exist
• https://github.com/twitter/algebird/
• Long, String, Set, Seq, Map, etc…
• HyperLogLog – Cardinality Estimates
• QTree – Quantile Estimates
• SpaceSaver/HeavyHitters – Approx Top-K
• Also easy to add your own with libraries
like stream-lib [C3]](https://image.slidesharecdn.com/buildingscalableaggregationsystems04152015-150415174319-conversion-gate02/85/Building-Scalable-Aggregation-Systems-16-320.jpg)
![Online Incremental Systems
• Twitter’s Summingbird [PA1, C4], Google’s Mesa [PA2],
Koverse’s Aggregation Framework
milliseconds seconds minutes
large many few
seconds minutes hours
billions millions thousands
Query Latency
# of Users
Freshness
Data Size
S
M
K](https://image.slidesharecdn.com/buildingscalableaggregationsystems04152015-150415174319-conversion-gate02/85/Building-Scalable-Aggregation-Systems-26-320.jpg)
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Building Scalable Aggregation Systems
- 1. Building Scalable Aggregation Systems Boulder/Denver Big Data Meetup April 15, 2015 Jared Winick jaredwinick @ koverse.com @jaredwinick
- 2. Outline • The Value of Aggregations • Abstractions • Systems • Demo • References/Additional Information
- 5. Aggregation provides a means of turning billions of pieces of raw data into condensed, human-consumable information.
- 6. Aggregation of Aggregations Time Series Set Size/Cardinality Top-K Quantiles Density/Heatmap 16.3k Unique Users G1 G2
- 8. Abstractions
- 10. 1 2 3 4 3 + + = 7 = 10 = + We can parallelize integer addition
- 11. Associative + Commutative Operations • Associative: 1 + (2 + 3) = (1 + 2) + 3 • Commutative: 1 + 2 = 2 + 1 • Allows us to parallelize our reduce (for instance locally in combiners) • Applies to many operations, not just integer addition. • Spoiler: Key to incremental aggregations
- 12. {a, b} {b, c} {a, c} {a} {a, b, c} + + = {a, c} = {a, b, c} = + We can also parallelize the “addition” of other types, like Sets, as Set Union is associative
- 13. Monoid Interface • Abstract Algebra provides a formal foundation for what we can casually observe. • Don’t be thrown off by the name, just think of it as another trait/interface. • Monoids provide a critical abstraction to treat aggregations of different types in the same way
- 14. Monoid Examples
- 15. Monoid Examples
- 16. Many Monoid Implementations Already Exist • https://github.com/twitter/algebird/ • Long, String, Set, Seq, Map, etc… • HyperLogLog – Cardinality Estimates • QTree – Quantile Estimates • SpaceSaver/HeavyHitters – Approx Top-K • Also easy to add your own with libraries like stream-lib [C3]
- 17. Serialization • One additional trait we need our “aggregatable” types to have is that we can serialize/deserialize them. 1 2 3 4 3 + + = 7 = 1 0 = + 1) zero() 2) plus() 3) plus() 4) serialize() 6) deserialize() 5) zero() 7) plus() 9) plus() 3 78) deserialize()
- 18. These abstractions enable a small library of reusable code to aggregate data in many parts of your system.
- 19. Systems
- 20. Requirements and Tradeoffs Query Latency milliseconds seconds minutes • Results are pre-computed • requires compute and storage resources • Supported queries must be known in advance • Results are computed at query time • No resources used except for executed queries • Ad-hoc queries
- 21. Requirements and Tradeoffs Number of Users large many few • Resources required per query must be small • Requires scalable query handling/storage • Queries can be expensive
- 22. Requirements and Tradeoffs Freshness of Results seconds minutes hours • May require streaming platform in addition to batch • Smaller, more frequent updates is more work • Single batch platform • Less frequent computation
- 23. Requirements and Tradeoffs Amount of Data billions millions thousands • Requires parallelized computation and storage • Single server is sufficient
- 24. SQL on Hadoop • Impala, Hive, SparkSQL milliseconds seconds minutes large many few seconds minutes hours billions millions thousands Query Latency # of Users Freshness Data Size
- 25. Batch Jobs • Spark, Hadoop MapReduce milliseconds seconds minutes large many few seconds minutes hours billions millions thousands Query Latency # of Users Freshness Data Size Dependent on where you put the job’s output
- 26. Online Incremental Systems • Twitter’s Summingbird [PA1, C4], Google’s Mesa [PA2], Koverse’s Aggregation Framework milliseconds seconds minutes large many few seconds minutes hours billions millions thousands Query Latency # of Users Freshness Data Size S M K
- 27. Online Incremental Systems: Common Components • Aggregations are computed/reduced incrementally via associative operations • Results are mostly pre-computed for so queries are inexpensive • Aggregations, keyed by dimensions, are stored in low latency, scalable key-value store
- 28. Summingbird Program Summingbird Data HDFS Queues Storm Topology Hadoop Job Online KV store Batch KV store Client Library Client Reduce Reduce Reduce Reduce
- 30. Koverse Data Apache Accumulo Koverse Server Hadoop Job Reduce Reduce ClientRecords Aggregates Min/Maj Compation Iterator Reduce Scan Iterator Reduce
- 31. Demo
- 32. References Presentations P1. Algebra for Analytics - https://speakerdeck.com/johnynek/algebra-for-analytics Code C1. Algebird - https://github.com/twitter/algebird C2. Simmer - https://github.com/avibryant/simmer C3. stream-lib https://github.com/addthis/stream-lib C4. Summingbird - https://github.com/twitter/summingbird Papers PA1. Summingbird: A Framework for Integrating Batch and Online MapReduce Computations http://www.vldb.org/pvldb/vol7/p1441-boykin.pdf PA2. Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42851.pdf PA3. Monoidify! Monoids as a Design Principle for Efficient MapReduce Algorithms http://arxiv.org/abs/1304.7544 Video V1. Intro To Summingbird - https://engineering.twitter.com/university/videos/introduction-to-summingbird Graphics G1. Histogram Graphic - http://www.statmethods.net/graphs/density.html G2. Heatmap Graphic - https://www.mapbox.com/blog/twitter-map-every-tweet/ G3. The Matrix Background - http://wall.alphacoders.com/by_sub_category.php?id=198802