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lsar.pulsar-client-all.3.3.1.source-code.overview.html Maven / Gradle / Ivy

   

Avro is a data serialization system.

  
Overview

  
Avro provides:
    

      
Rich data structures.
      
A compact, fast, binary data format.
      
A container file, to store persistent data.
      
Remote procedure call (RPC).
      
Simple integration with dynamic languages.  Code generation
      is not required to read or write data files nor to use or
      implement RPC protocols.  Code generation as an optional
      optimization, only worth implementing for statically typed
      languages.
    
  

  
Schemas

  
Avro relies on {@link org.apache.avro.Schema schemas}.
  When Avro data is read, the schema used when writing it is always
  present.  This permits each datum to be written with no per-value
  overheads, making serialization both fast and small.  This also
  facilitates use with dynamic, scripting languages, since data,
  together with its schema, is fully self-describing.

  
When Avro data is stored in a {@link
  org.apache.avro.file.DataFileWriter file}, its schema is stored with
  it, so that files may be processed later by any program.  If the
  program reading the data expects a different schema this can be
  easily resolved, since both schemas are present.

  
When Avro is used in {@link org.apache.avro.ipc RPC}, the client
    and server exchange schemas in the connection handshake.  (This
    can be optimized so that, for most calls, no schemas are actually
    transmitted.)  Since both client and server both have the other's
    full schema, correspondence between same named fields, missing
    fields, extra fields, etc. can all be easily resolved.

  
Avro schemas are defined with
  with JSON .  This facilitates
  implementation in languages that already have JSON libraries.

  
Comparison with other systems

  Avro provides functionality similar to systems such
  as Thrift,
  Protocol Buffers,
  etc.  Avro differs from these systems in the following fundamental
  aspects.
  

    
Dynamic typing: Avro does not require that code be
    generated.  Data is always accompanied by a schema that permits
    full processing of that data without code generation, static
    datatypes, etc.  This facilitates construction of generic
    data-processing systems and languages.
    
Untagged data: Since the schema is present when data is
    read, considerably less type information need be encoded with
    data, resulting in smaller serialization size.
    
No manually-assigned field IDs: When a schema changes,
    both the old and new schema are always present when processing
    data, so differences may be resolved symbolically, using field
    names.