Goavro is a library that encodes and decodes Avro data.
- Encodes to and decodes from both binary and textual JSON Avro data.
Codecis stateless and is safe to use by multiple goroutines.
With the exception of features not yet supported, goavro attempts to be fully compliant with the most recent version of the Avro specification.
All usage of gopkg.in has been removed in favor of Go modules.
Please update your import paths to github.com/linkedin/goavro/v2. v1
users can still use old versions of goavro by adding a constraint to
your go.mod or Gopkg.toml file.
require (
github.com/linkedin/goavro v1.0.5
)
[[constraint]]
name = "github.com/linkedin/goavro"
version = "=1.0.5"The original version of this library was written prior to my really understanding how Avro namespaces ought to work. After using Avro for a long time now, and after a lot of research, I think I grok Avro namespaces properly, and the library now correctly handles every test case the Apache Avro distribution has for namespaces, including being able to refer to a previously defined data type later on in the same schema.
The original version of this library required creating goavro.Record
instances, and use of getters and setters to access a record's
fields. When schemas were complex, this required a lot of work to
debug and get right. The original version also required users to break
schemas in chunks, and have a different schema for each record
type. This was cumbersome, annoying, and error prone.
The new version of this library eliminates the goavro.Record type,
and accepts a native Go map for all records to be encoded. Keys are
the field names, and values are the field values. Nothing could be
more easy. Conversely, decoding Avro data yields a native Go map for
the upstream client to pull data back out of.
Furthermore, there is never a reason to ever have to break your schema
down into record schemas. Merely feed the entire schema into the
NewCodec function once when you create the Codec, then use
it. This library knows how to parse the data provided to it and ensure
data values for records and their fields are properly encoded and
decoded.
The original version of this library was truly written with Go's idea
of io.Reader and io.Writer composition in mind. Although
composition is a powerful tool, the original library had to pull bytes
off the io.Reader--often one byte at a time--check for read errors,
decode the bytes, and repeat. This version, by using a native Go byte
slice, both decoding and encoding complex Avro data here at LinkedIn
is between three and four times faster than before.
The original version of this library did not support JSON encoding or decoding, because it wasn't deemed useful for our internal use at the time. When writing the new version of the library I decided to tackle this issue once and for all, because so many engineers needed this functionality for their work.
The original version of this library did not well handle default values for record fields. This version of the library uses a default value of a record field when encoding from native Go data to Avro data and the record field is not specified. Additionally, when decoding from Avro JSON data to native Go data, and a field is not specified, the default value will be used to populate the field.
If you have the ability to rebuild and redeploy your software whenever data schemas change, code generation tools might be the best solution for your application.
There are numerous excellent tools for generating source code to
translate data between native and Avro binary or textual data. One
such tool is linked below. If a particular application is designed to
work with a rarely changing schema, programs that use code generated
functions can potentially be more performant than a program that uses
goavro to create a Codec dynamically at run time.
I recommend benchmarking the resultant programs using typical data using both the code generated functions and using goavro to see which performs better. Not all code generated functions will out perform goavro for all data corpuses.
If you don't have the ability to rebuild and redeploy software updates
whenever a data schema change occurs, goavro could be a great fit for
your needs. With goavro, your program can be given a new schema while
running, compile it into a Codec on the fly, and immediately start
encoding or decoding data using that Codec. Because Avro encoding
specifies that encoded data always be accompanied by a schema this is
not usually a problem. If the schema change is backwards compatible,
and the portion of your program that handles the decoded data is still
able to reference the decoded fields, there is nothing that needs to
be done when the schema change is detected by your program when using
goavro Codec instances to encode or decode data.
Documentation is available via
package main
import (
"fmt"
"github.com/linkedin/goavro/v2"
)
func main() {
codec, err := goavro.NewCodec(`
{
"type": "record",
"name": "LongList",
"fields" : [
{"name": "next", "type": ["null", "LongList"], "default": null}
]
}`)
if err != nil {
fmt.Println(err)
}
// NOTE: May omit fields when using default value
textual := []byte(`{"next":{"LongList":{}}}`)
// Convert textual Avro data (in Avro JSON format) to native Go form
native, _, err := codec.NativeFromTextual(textual)
if err != nil {
fmt.Println(err)
}
// Convert native Go form to binary Avro data
binary, err := codec.BinaryFromNative(nil, native)
if err != nil {
fmt.Println(err)
}
// Convert binary Avro data back to native Go form
native, _, err = codec.NativeFromBinary(binary)
if err != nil {
fmt.Println(err)
}
// Convert native Go form to textual Avro data
textual, err = codec.TextualFromNative(nil, native)
if err != nil {
fmt.Println(err)
}
// NOTE: Textual encoding will show all fields, even those with values that
// match their default values
fmt.Println(string(textual))
// Output: {"next":{"LongList":{"next":null}}}
}Also please see the example programs in the examples directory for
reference.
The ab2t program is similar to the reference standard
avrocat program and converts Avro OCF files to Avro JSON
encoding.
The Avro-ReWrite program, arw, can be used to rewrite an
Avro OCF file while optionally changing the block counts, the
compression algorithm. arw can also upgrade the schema provided the
existing datum values can be encoded with the newly provided schema.
The Avro Header program, avroheader, can be used to print various
header information from an OCF file.
The splice program can be used to splice together an OCF file from
an Avro schema file and a raw Avro binary data file.
A Codec provides four methods for translating between a byte slice
of either binary or textual Avro data and native Go data.
The following methods convert data between native Go data and byte slices of the binary Avro representation:
BinaryFromNative
NativeFromBinary
The following methods convert data between native Go data and byte slices of the textual Avro representation:
NativeFromTextual
TextualFromNative
Each Codec also exposes the Schema method to return a simplified
version of the JSON schema string used to create the Codec.
Goavro does not use Go's structure tags to translate data between native Go types and Avro encoded data.
When translating from either binary or textual Avro to native Go data, goavro returns primitive Go data values for corresponding Avro data values. The table below shows how goavro translates Avro types to Go types.
| Avro | Go |
|---|---|
null |
nil |
boolean |
bool |
bytes |
[]byte |
float |
float32 |
double |
float64 |
long |
int64 |
int |
int32 |
string |
string |
array |
[]interface{} |
enum |
string |
fixed |
[]byte |
map and record |
map[string]interface{} |
union |
see below |
Because of encoding rules for Avro unions, when an union's value is
null, a simple Go nil is returned. However when an union's value
is non-nil, a Go map[string]interface{} with a single key is
returned for the union. The map's single key is the Avro type name and
its value is the datum's value.
Goavro does not use Go's structure tags to translate data between native Go types and Avro encoded data.
When translating from native Go to either binary or textual Avro data,
goavro generally requires the same native Go data types as the decoder
would provide, with some exceptions for programmer convenience. Goavro
will accept any numerical data type provided there is no precision
lost when encoding the value. For instance, providing float64(3.0)
to an encoder expecting an Avro int would succeed, while sending
float64(3.5) to the same encoder would return an error.
When providing a slice of items for an encoder, the encoder will
accept either []interface{}, or any slice of the required type. For
instance, when the Avro schema specifies:
{"type":"array","items":"string"}, the encoder will accept either
[]interface{}, or []string. If given []int, the encoder will
return an error when it attempts to encode the first non-string array
value using the string encoder.
When providing a value for an Avro union, the encoder will accept
nil for a null value. If the value is non-nil, it must be a
map[string]interface{} with a single key-value pair, where the key
is the Avro type name and the value is the datum's value. As a
convenience, the Union function wraps any datum value in a map as
specified above.
func ExampleUnion() {
codec, err := goavro.NewCodec(`["null","string","int"]`)
if err != nil {
fmt.Println(err)
}
buf, err := codec.TextualFromNative(nil, goavro.Union("string", "some string"))
if err != nil {
fmt.Println(err)
}
fmt.Println(string(buf))
// Output: {"string":"some string"}
}Goavro is a fully featured encoder and decoder of binary and textual JSON Avro data. It fully supports recursive data structures, unions, and namespacing. It does have a few limitations that have yet to be implemented.
The Avro specification allows an implementation to optionally map a writer's schema to a reader's schema using aliases. Although goavro can compile schemas with aliases, it does not yet implement this feature.
Kafka is the reason goavro was written. Similar to Avro Object Container Files being a layer of abstraction above Avro Data Serialization format, Kafka's use of Avro is a layer of abstraction that also sits above Avro Data Serialization format, but has its own schema. Like Avro Object Container Files, this has been implemented but removed until the API can be improved.
When decoding arrays, maps, and OCF files, the Avro specification states that the binary includes block counts and block sizes that specify how many items are in the next block, and how many bytes are in the next block. To prevent possible denial-of-service attacks on clients that use this library caused by attempting to decode maliciously crafted data, decoded block counts and sizes are compared against public library variables MaxBlockCount and MaxBlockSize. When the decoded values exceed these values, the decoder returns an error.
Because not every upstream client is the same, we've chosen some sane
defaults for these values, but left them as mutable variables, so that
clients are able to override if deemed necessary for their
purposes. Their initial default values are (math.MaxInt32 or
~2.2GB).
Please see my reasons why schema evolution is broken for Avro 1.x.
Copyright 2017 LinkedIn Corp. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
Copyright (c) 2011 The Snappy-Go Authors. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Goavro links with Google Snappy to provide Snappy compression and decompression support.