Apache AVRO is a data serialization system that includes a schema language, compact serialized format, RPC framework, and APIs in multiple languages. Its goals are to support cross-language data exchange and simple but expressive schema evolution. It provides a way to define data schemas and protocols and serialize data in a compact binary format. The serialization allows for dynamic schema evolution while preserving compatibility. AVRO integrates with Hadoop and provides tools for working with AVRO data and schemas. Version 1.3 is planned for release soon with evolving but stable APIs and formats.

1. Apache AVRO
What's new?
Philip Zeyliger, Cloudera
(AVRO committer)
Boston HUG
January 19, 2009

2. What's AVRO?
A data serialization system
Includes:
A schema language
A compact serialized form
An RPC framework
A handful of APIs, in a handful of languages
Goals:
Cross-language
Support for dynamic access
Simple but expressive schema evolution
Same "space" as Apache Thrift, Google Protocol Buffers,
Binary JSON, and XDR. Subtle differences with all of them.

3. AVRO Protocols & Schemas
@namespace("org.apache.avro.demo")
protocol CurrencyConversion {
enum Currency {
USD,
GBP,
EUR,
JPY
}
record Money {
Currency currency;
int amount;
}
error UnknownRateError {
Currency currency;
}
Money convert(Money input, Currency targetCurrency)
throws UnknownRateError;
double rate(Currency input, Currency output) throws UnknownRateError;
}
"genavro" IDL (AVRO-258)

4. $java -jar avro-tools-1.2.0-dev.jar genavro < demo.genavro "messages" : {
{ "convert" : {
"protocol" : "CurrencyConversion", "request" : [ {
"namespace" : "org.apache.avro.demo", "name" : "input",
"types" : [ { "type" : "Money"
"type" : "enum", }, {
"name" : "Currency", "name" : "targetCurrency",
"symbols" : [ "USD", "GBP", "EUR", "JPY" ] "type" : "Currency"
}, { } ],
"type" : "record", "response" : "Money",
"name" : "Money", "errors" : [ "UnknownRateError" ]
"fields" : [ { },
"name" : "currency", "rate" : {
"type" : "Currency" "request" : [ {
}, { "name" : "input",
"name" : "amount", "type" : "Currency"
"type" : "int" }, {
}] "name" : "output",
}, { "type" : "Currency"
"type" : "error", } ],
"name" : "UnknownRateError", "response" : "double",
"fields" : [ { "errors" : [ "UnknownRateError" ]
"name" : "currency", }
"type" : "Currency" }
}] }[
} ],
JSON Representation of Protocol and Schemas

5. Types
primitive complex
string record
bytes array
int & long map: string -> T
float & double union
boolean fixed<N>
null enum

6. Schema Evolution & Projection
AVRO binary data never travels without its schema. This
allows dynamic tooling.
Writer's Schema and Reader's Schema may be different.
{ /* Writer */ { /* Reader */
"type" : "record", "type" : "record",
"name" : "Person", "name" : "Person",
"fields" : [ { "fields" : [ {
"name" : "first", "name" : "first",
"type" : "string" "type" : "string"
}, { }, {
"name" : "sport", "name" : "age",
"type" : "string", "type" : "int",
} "default": 0,
} }
}
Serialized Data: Data presented to application:
"Alice", "Ultimate Frisbee" "Alice", 0

7. APIs
Python
Dynamic
Java
Specific (generated code)
Generic (container-based)
Reflection (induces schemas from classes)
C
C++
Ruby

8. C API
char buf[64];
avro_writer_t writer = avro_writer_memory(buf, sizeof(buf));
avro_schema_t writers_schema = avro_schema_string();
avro_datum_t datum = avro_string("Hello, world!");
avro_write_data(writer, writers_schema, datum);
avro_reader_t reader = avro_reader_memory(buf, sizeof(buf));
avro_schema_t readers_schema = avro_schema_string();
avro_datum_t read_datum;
avro_read_data(reader, writers_schema, readers_schema, &read_datum);

9. Data File Format
(AVRO-160)
Features:
* Splittable
(important for Hadoop!)
* Append only with same
schema.
* Compression
* Arbitrary metadata
* Simple

10. Hadoop Integration
Users
AvroInputFormat/AvroOutputFormat (MR-815)
Using AVRO in the shuffle (MR-1126)
Note that AVRO schemas let you specify sort order;
binary comparators are a thing of the past
Many Writables can be AVRO+Reflection instead
AVRO sort order leaves hand-writing RawComparators in
the past; for Streaming, you now get fast comparators for
free!
Framework
AVRO for Hadoop RPC (e.g., HDFS-982)
Goals
Open up protocols for cross-language use

11. avro-tools
Available tools:
compile Generates Java code for the given schema.
fragtojson Renders a binary-encoded Avro datum as JSON.
fromjson Reads JSON records and writes an Avro data file.
genavro Generates a JSON schema from a GenAvro file
getschema Prints out schema of an Avro data file.
induce Induce a schema/protocol from Java class/interface.
jsontofrag Renders a JSON-encoded Avro datum as binary.
rpcreceive Opens an HTTP RPC Server and listens for one message.
rpcsend Sends a single RPC message.
tojson Dumps an Avro data file as JSON, one record per line.

12. 1.3 to be released soon...
Good time to try it out!
What's evolving?
Trying not to evolve the serialized format.
APIs are evolving.
Transports are evolving.

13. Obligatory Links
Web page:
http://hadoop.apache.org/avro/
Mailing list:
avro-user-subscribe@hadoop.apache.org
Source repository:
http://svn.apache.org/repos/asf/hadoop/avro/

14. Thanks!
Questions?
Philip Zeyliger
philip@cloudera.com