Apache Avro

Apache Avro
Developer(s) Apache Software Foundation
Stable release
1.8.2 / May 20, 2017 (2017-05-20)
Repository git-wip-us.apache.org/repos/asf/avro.git
Development status Active
Type remote procedure call framework
License Apache License 2.0
Website avro.apache.org

Avro is a remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format. Its primary use is in Apache Hadoop, where it can provide both a serialization format for persistent data, and a wire format for communication between Hadoop nodes, and from client programs to the Hadoop services.

It is similar to Thrift and Protocol Buffers, but does not require running a code-generation program when a schema changes (unless desired for statically-typed languages).

Apache Spark SQL can access Avro as a data source.[1]

Avro Object Container File[2]

An Avro Object Container File consists of:

A file header consists of:

For data blocks Avro specifies two serialization encodings:[3] binary and JSON. Most applications will use the binary encoding, as it is smaller and faster. For debugging and web-based applications, the JSON encoding may sometimes be appropriate.

Schema Definition[4]

Avro schemas are defined using JSON. Schemas are composed of primitive types (null, boolean, int, long, float, double, bytes, and string) and complex types (record, enum, array, map, union, and fixed).

Simple schema example:

 {
   "namespace": "example.avro",
   "type": "record",
   "name": "User",
   "fields": [
      {"name": "name", "type": "string"},
      {"name": "favorite_number",  "type": ["int", "null"]},
      {"name": "favorite_color", "type": ["string", "null"]}
   ] 
 }

Serializing and Deserializing

Data in Avro might be stored with its corresponding schema, meaning a serialized item can be read without knowing the schema ahead of time.

Example serialization and deserialization code in Python[5]

Serialization:

import avro.schema
from avro.datafile import DataFileReader, DataFileWriter
from avro.io import DatumReader, DatumWriter

schema = avro.schema.parse(open("user.avsc").read())  # need to know the schema to write

writer = DataFileWriter(open("users.avro", "w"), DatumWriter(), schema)
writer.append({"name": "Alyssa", "favorite_number": 256})
writer.append({"name": "Ben", "favorite_number": 7, "favorite_color": "red"})
writer.close()

File "users.avro" will contain the schema in JSON and a compact binary representation[6] of the data:

$ od -c users.avro
0000000    O   b   j 001 004 026   a   v   r   o   .   s   c   h   e   m
0000020    a 272 003   {   "   t   y   p   e   "   :       "   r   e   c
0000040    o   r   d   "   ,       "   n   a   m   e   s   p   a   c   e
0000060    "   :       "   e   x   a   m   p   l   e   .   a   v   r   o
0000100    "   ,       "   n   a   m   e   "   :       "   U   s   e   r
0000120    "   ,       "   f   i   e   l   d   s   "   :       [   {   "
0000140    t   y   p   e   "   :       "   s   t   r   i   n   g   "   ,
0000160        "   n   a   m   e   "   :       "   n   a   m   e   "   }
0000200    ,       {   "   t   y   p   e   "   :       [   "   i   n   t
0000220    "   ,       "   n   u   l   l   "   ]   ,       "   n   a   m
0000240    e   "   :       "   f   a   v   o   r   i   t   e   _   n   u
0000260    m   b   e   r   "   }   ,       {   "   t   y   p   e   "   :
0000300        [   "   s   t   r   i   n   g   "   ,       "   n   u   l
0000320    l   "   ]   ,       "   n   a   m   e   "   :       "   f   a
0000340    v   o   r   i   t   e   _   c   o   l   o   r   "   }   ]   }
0000360  024   a   v   r   o   .   c   o   d   e   c  \b   n   u   l   l
0000400   \0 211 266   / 030 334   ˪  **   P 314 341 267 234 310   5 213
0000420    6 004   ,  \f   A   l   y   s   s   a  \0 200 004 002 006   B
0000440    e   n  \0 016  \0 006   r   e   d 211 266   / 030 334   ˪  **
0000460    P 314 341 267 234 310   5 213   6
0000471

Deserialization:

reader = DataFileReader(open("users.avro", "r"), DatumReader())  # no need to know the schema to read
for user in reader:
    print user
reader.close()

This outputs:

{u'favorite_color': None, u'favorite_number': 256, u'name': u'Alyssa'}
{u'favorite_color': u'red', u'favorite_number': 7, u'name': u'Ben'}

Languages with APIs

Though theoretically any language could use Avro, the following languages have APIs written for them:[7][8]

Avro IDL

In addition to supporting JSON for type and protocol definitions, Avro includes experimental[13] support for an alternative interface description language (IDL) syntax known as Avro IDL. Previously known as GenAvro, this format is designed to ease adoption by users familiar with more traditional IDLs and programming languages, with a syntax similar to C/C++, Protocol Buffers and others.

See also

References

  1. ↑ http://dataconomy.com/3-reasons-hadoop-analytics-big-deal/
  2. ↑ "Apache Avro™ Specification: Object Container Files". avro.apache.org. Retrieved 2016-09-27.
  3. ↑ "Apache Avro™ Specification: Encodings". avro.apache.org. Retrieved 2016-09-27.
  4. ↑ "Apache Avro™ Getting Started (Python)". avro.apache.org. Retrieved 2016-06-16.
  5. ↑ "Apache Avro™ Getting Started (Python)". avro.apache.org. Retrieved 2016-06-16.
  6. ↑ "Apache Avro™ Specification: Data Serialization". avro.apache.org. Retrieved 2016-06-16.
  7. ↑ phunt. "GitHub - phunt/avro-rpc-quickstart: Apache Avro RPC Quick Start. Avro is a subproject of Apache Hadoop.". GitHub. Retrieved April 13, 2016.
  8. ↑ "Supported Languages - Apache Avro - Apache Software Foundation". Retrieved 2016-04-21.
  9. ↑ "Avro: 1.5.1 - ASF JIRA". Retrieved April 13, 2016.
  10. ↑ "[AVRO-533] .NET implementation of Avro - ASF JIRA". Retrieved April 13, 2016.
  11. ↑ "Supported Languages". Retrieved April 13, 2016.
  12. ↑ "Native Haskell implementation of Avro". Thomas M. DuBuisson, Galois, Inc. Retrieved August 8, 2016.
  13. ↑ "Apache Avro 1.8.0 IDL". Retrieved April 13, 2016.

Further reading


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