ApacheCon09: Avro
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The document discusses the state of Hadoop in 2009 and goals for 2010 and beyond. It focuses on improving data formats by proposing the use of Apache Avro, which provides an expressive, compact, and dynamic data serialization system with built-in capabilities for data sharing, remote procedure calls, and schema evolution. The document outlines how Avro could address current issues with data formats in Hadoop and its potential inclusion in future Hadoop releases.
![2009 Hadoop Milestones
● A 2009 Sort Champion (O'Malley)
● won 100TB “Gray” sort @ .578TB/minute
● won “Minute” sort with 500GB
● Split Core Project in Three
● Common, HDFS & MapReduce
● Released 0.18.3, 0.19.[0-2], 0.20.[0-1]
● Many meetups, conferences, etc.
● Yada, yada, yada.](https://image.slidesharecdn.com/acus09-091112125244-phpapp01/85/ApacheCon09-Avro-2-320.jpg)
ApacheCon09: Avro
- 1. State of the Elephant Doug Cutting Cloudera
- 2. 2009 Hadoop Milestones ● A 2009 Sort Champion (O'Malley) ● won 100TB “Gray” sort @ .578TB/minute ● won “Minute” sort with 500GB ● Split Core Project in Three ● Common, HDFS & MapReduce ● Released 0.18.3, 0.19.[0-2], 0.20.[0-1] ● Many meetups, conferences, etc. ● Yada, yada, yada.
- 3. Goals for 2010 and beyond ● IMHO, YMMV, IANAM, etc. ● Concrete ● 1.0 release – compatible APIs & RPCs for > 1 year – Kerberos-based authentication ● Abstract ● faster, more reliable, available ● easier sharing – of data & hardware resources ● spreadsheet-like interfaces – provide non-programmers – with powerful, interactive tools
- 4. Abstract Requirements ● security ● facilitate sharing of resources ● stable cross-language APIs ● facilitate diverse tools & apps ● expressive, inter-operable data ● facilitates sharing of datasets ● facilitates dynamic analyses
- 5. Data Formats ● today in Hadoop: ● text – pro: inter-operable – con: not expressive, inefficient ● Java Writable – pro: expressive, efficient – con: platform-specific, fragile
- 6. Protocol Buffers & Thrift ● expressive ● efficient (small & fast) ● but not very dynamic ● cannot browse arbitrary data ● no DESCRIBE or SHOW ● viewing a new dataset – requires code generation & load ● writing a new dataset – requires generating schema text – plus code generation & load
- 7. Avro Data ● as expressive ● smaller and faster ● dynamic ● schema stored with data – but factored out of instances ● APIs permit reading & creating – arbitrary datatypes – without generating & loading code
- 8. Avro Data ● includes a file format ● includes a textual encoding ● handles versioning ● if schema changes ● can still process data ● hope Hadoop apps will ● upgrade from text; & ● and standardize on Avro for data
- 9. Avro RPC ● leverage versioning support ● to permit different versions of services to interoperate ● for Hadoop services, will ● provide cross-language access ● let apps talk to clusters running different versions
- 10. Avro Status ● Java & Python APIs ● C & C++ APIs making rapid progress ● 1.1 release ● added JSON data and comparators ● 1.2 release ● added HTTP & UDP-based RPC ● included in Hadoop 0.21 ● as format for job history ● in sequence files
- 11. Avro Near Future ● full mapreduce support for Avro data ● enables fast comparators for non-Java apps ● Avro RPC used in Hadoop 0.22 (1.0)? ● provides compatibility; & ● native access from non-Java