parquet-go v1.4.0

parquet-go is a pure-go implementation of reading and writing the parquet format file.

• Support Read/Write Nested/Flat Parquet File
• Simple to use
• High performance

Install

Add the parquet-go library to your $GOPATH/src and install dependencies:

go get github.com/xitongsys/parquet-go
cd $GOPATH/src/github.com/xitongsys/parquet-go/
dep ensure

Look at examples in example/.

cd $GOPATH/src/github.com/xitongsys/parquet-go/example
go run local_flat.go

Type

There are two types in Parquet: Primitive Type and Logical Type. Logical types are stored as primitive types. The following list is the currently implemented data types:

Parquet Type	Primitive Type	Go Type
BOOLEAN	BOOLEAN	bool
INT32	INT32	int32
INT64	INT64	int64
INT96	INT96	string
FLOAT	FLOAT	float32
DOUBLE	DOUBLE	float64
BYTE_ARRAY	BYTE_ARRAY	string
FIXED_LEN_BYTE_ARRAY	FIXED_LEN_BYTE_ARRAY	string
UTF8	BYTE_ARRAY	string
INT_8	INT32	int32
INT_16	INT32	int32
INT_32	INT32	int32
INT_64	INT64	int64
UINT_8	INT32	uint32
UINT_16	INT32	uint32
UINT_32	INT32	uint32
UINT_64	INT64	uint64
DATE	INT32	int32
TIME_MILLIS	INT32	int32
TIME_MICROS	INT64	int64
TIMESTAMP_MILLIS	INT64	int64
TIMESTAMP_MICROS	INT64	int64
INTERVAL	FIXED_LEN_BYTE_ARRAY	string
DECIMAL	INT32,INT64,FIXED_LEN_BYTE_ARRAY,BYTE_ARRAY	int32,int64,string,string
LIST		slice
MAP		map

Tips

• Although DECIMAL can be stored as INT32,INT64,FIXED_LEN_BYTE_ARRAY,BYTE_ARRAY, Currently I suggest to use FIXED_LEN_BYTE_ARRAY.

Encoding

PLAIN:

All types

PLAIN_DICTIONARY:

All types

DELTA_BINARY_PACKED:

INT32, INT64, INT_8, INT_16, INT_32, INT_64, UINT_8, UINT_16, UINT_32, UINT_64, TIME_MILLIS, TIME_MICROS, TIMESTAMP_MILLIS, TIMESTAMP_MICROS

DELTA_BYTE_ARRAY:

BYTE_ARRAY, UTF8

DELTA_LENGTH_BYTE_ARRAY:

BYTE_ARRAY, UTF8

Tips

• Some platforms don't support all kinds of encodings. If you are not sure, just use PLAIN and PLAIN_DICTIONARY.

Repetition Type

There are three repetition types in Parquet: REQUIRED, OPTIONAL, REPEATED.

Repetition Type	Example	Description
REQUIRED	V1 int32 `parquet:"name=v1, type=INT32"`	No extra description
OPTIONAL	V1 *int32 `parquet:"name=v1, type=INT32"`	Declare as pointer
REPEATED	V1 []int32 `parquet:"name=v1, type=INT32, repetitontype=REPEATED"`	Add 'repetitiontype=REPEATED' in tags

Tips

• The difference between a List and a REPEATED variable is the 'repetitiontype' in tags. Although both of them are stored as slice in go, they are different in parquet. You can find the detail of List in parquet at here. I suggest just use a List.
• For LIST and MAP, some existed parquet files use some nonstandard formats(see here). For standard format, parquet-go will convert them to go slice and go map. For nonstandard formats, parquet-go will convert them to corresponding structs.

Example of Type and Encoding

Bool              bool    `parquet:"name=bool, type=BOOLEAN"`
Int32             int32   `parquet:"name=int32, type=INT32"`
Int64             int64   `parquet:"name=int64, type=INT64"`
Int96             string  `parquet:"name=int96, type=INT96"`
Float             float32 `parquet:"name=float, type=FLOAT"`
Double            float64 `parquet:"name=double, type=DOUBLE"`
ByteArray         string  `parquet:"name=bytearray, type=BYTE_ARRAY"`
FixedLenByteArray string  `parquet:"name=FixedLenByteArray, type=FIXED_LEN_BYTE_ARRAY, length=10"`

Utf8            string `parquet:"name=utf8, type=UTF8, encoding=PLAIN_DICTIONARY"`
Int_8           int32  `parquet:"name=int_8, type=INT_8"`
Int_16          int32  `parquet:"name=int_16, type=INT_16"`
Int_32          int32  `parquet:"name=int_32, type=INT_32"`
Int_64          int64  `parquet:"name=int_64, type=INT_64"`
Uint_8          uint32 `parquet:"name=uint_8, type=UINT_8"`
Uint_16         uint32 `parquet:"name=uint_16, type=UINT_16"`
Uint_32         uint32 `parquet:"name=uint_32, type=UINT_32"`
Uint_64         uint64 `parquet:"name=uint_64, type=UINT_64"`
Date            int32  `parquet:"name=date, type=DATE"`
TimeMillis      int32  `parquet:"name=timemillis, type=TIME_MILLIS"`
TimeMicros      int64  `parquet:"name=timemicros, type=TIME_MICROS"`
TimestampMillis int64  `parquet:"name=timestampmillis, type=TIMESTAMP_MILLIS"`
TimestampMicros int64  `parquet:"name=timestampmicros, type=TIMESTAMP_MICROS"`
Interval        string `parquet:"name=interval, type=INTERVAL"`

Decimal1 int32  `parquet:"name=decimal1, type=DECIMAL, scale=2, precision=9, basetype=INT32"`
Decimal2 int64  `parquet:"name=decimal2, type=DECIMAL, scale=2, precision=18, basetype=INT64"`
Decimal3 string `parquet:"name=decimal3, type=DECIMAL, scale=2, precision=10, basetype=FIXED_LEN_BYTE_ARRAY, length=12"`
Decimal4 string `parquet:"name=decimal4, type=DECIMAL, scale=2, precision=20, basetype=BYTE_ARRAY"`

Map      map[string]int32 `parquet:"name=map, type=MAP, keytype=UTF8, valuetype=INT32"`
List     []string         `parquet:"name=list, type=LIST, valuetype=UTF8"`
Repeated []int32          `parquet:"name=repeated, type=INT32, repetitiontype=REPEATED"`

Compression Type

Type	Support
CompressionCodec_UNCOMPRESSED	YES
CompressionCodec_SNAPPY	YES
CompressionCodec_GZIP	YES
CompressionCodec_LZO	NO
CompressionCodec_BROTLI	NO
CompressionCodec_LZ4	NO
CompressionCodec_ZSTD	YES

ParquetFile

Read/Write a parquet file need a ParquetFile interface implemented

type ParquetFile interface {
	io.Seeker
	io.Reader
	io.Writer
	io.Closer
	Open(name string) (ParquetFile, error)
	Create(name string) (ParquetFile, error)
}

Using this interface, parquet-go can read/write parquet file on different platforms. All the file sources are at parquet-go-source. Now it supports(local/hdfs/s3/gcs/memory).

Writer

Three Writers are supported: ParquetWriter, JSONWriter, CSVWriter.

• ParquetWriter is used to write predefined Golang structs. Example of ParquetWriter

• JSONWriter is used to write JSON strings Example of JSONWriter

• CSVWriter is used to write data format similar with CSV(not nested) Example of CSVWriter

Reader

Two Readers are supported: ParquetReader, ColumnReader

• ParquetReader is used to read predefined Golang structs Example of ParquetReader

• ColumnReader is used to read raw column data. The read function return 3 slices([value], [RepetitionLevel], [DefinitionLevel]) of the records. Example of ColumnReader

Tips

• If the parquet file is very big (even the size of parquet file is small, the uncompressed size may be very large), please don't read all rows at one time, which may induce the OOM. You can read a small portion of the data at a time like a stream-oriented file.

Schema

There are three methods to define the schema: go struct tags, Json, CSV metadata. Only items in schema will be written and others will be ignored.

Tag

type Student struct {
	Name   string  `parquet:"name=name, type=UTF8, encoding=PLAIN_DICTIONARY"`
	Age    int32   `parquet:"name=age, type=INT32"`
	Id     int64   `parquet:"name=id, type=INT64"`
	Weight float32 `parquet:"name=weight, type=FLOAT"`
	Sex    bool    `parquet:"name=sex, type=BOOLEAN"`
	Day    int32   `parquet:"name=day, type=DATE"`
}

Example of tags

JSON

JSON schema can be used to define some complicated schema, which can't be defined by tag.

type Student struct {
	Name    string
	Age     int32
	Id      int64
	Weight  float32
	Sex     bool
	Classes []string
	Scores  map[string][]float32

	Friends []struct {
		Name string
		Id   int64
	}
	Teachers []struct {
		Name string
		Id   int64
	}
}

var jsonSchema string = `
{
  "Tag": "name=parquet-go-root, repetitiontype=REQUIRED",
  "Fields": [
    {"Tag": "name=name, inname=Name, type=UTF8, repetitiontype=REQUIRED"},
    {"Tag": "name=age, inname=Age, type=INT32, repetitiontype=REQUIRED"},
    {"Tag": "name=id, inname=Id, type=INT64, repetitiontype=REQUIRED"},
    {"Tag": "name=weight, inname=Weight, type=FLOAT, repetitiontype=REQUIRED"},
    {"Tag": "name=sex, inname=Sex, type=BOOLEAN, repetitiontype=REQUIRED"},

    {"Tag": "name=classes, inname=Classes, type=LIST, repetitiontype=REQUIRED",
     "Fields": [{"Tag": "name=element, type=UTF8, repetitiontype=REQUIRED"}]
    },
    {
      "Tag": "name=scores, inname=Scores, type=MAP, repetitiontype=REQUIRED",
      "Fields": [
        {"Tag": "name=key, type=UTF8, repetitiontype=REQUIRED"},
        {"Tag": "name=value, type=LIST, repetitiontype=REQUIRED",
         "Fields": [{"Tag": "name=element, type=FLOAT, repetitiontype=REQUIRED"}]
        }
      ]
    },
    {
      "Tag": "name=friends, inname=Friends, type=LIST, repetitiontype=REQUIRED",
      "Fields": [
       {"Tag": "name=element, repetitiontype=REQUIRED",
        "Fields": [
         {"Tag": "name=name, inname=Name, type=UTF8, repetitiontype=REQUIRED"},
         {"Tag": "name=id, inname=Id, type=INT64, repetitiontype=REQUIRED"}
        ]}
      ]
    },
    {
      "Tag": "name=teachers, inname=Teachers, repetitiontype=REPEATED",
      "Fields": [
        {"Tag": "name=name, inname=Name, type=UTF8, repetitiontype=REQUIRED"},
        {"Tag": "name=id, inname=Id, type=INT64, repetitiontype=REQUIRED"}
      ]
    }
  ]
}
`

Example of JSON schema

CSV metadata

md := []string{
	"name=Name, type=UTF8, encoding=PLAIN_DICTIONARY",
	"name=Age, type=INT32",
	"name=Id, type=INT64",
	"name=Weight, type=FLOAT",
	"name=Sex, type=BOOLEAN",
}

Example of CSV metadata

Parallel

Read/Write initial functions have a parallel parameters np which is the number of goroutines in reading/writing.

func NewParquetReader(pFile ParquetFile.ParquetFile, obj interface{}, np int64) (*ParquetReader, error)
func NewParquetWriter(pFile ParquetFile.ParquetFile, obj interface{}, np int64) (*ParquetWriter, error)
func NewJSONWriter(jsonSchema string, pfile ParquetFile.ParquetFile, np int64) (*JSONWriter, error)
func NewCSVWriter(md []string, pfile ParquetFile.ParquetFile, np int64) (*CSVWriter, error)

Examples

Example file	Descriptions
local_flat.go	write/read parquet file with no nested struct
local_nested.go	write/read parquet file with nested struct
read_partial.go	read partial fields from a parquet file
read_partial2.go	read sub-struct from a parquet file
read_without_schema_predefined.go	read a parquet file and no struct/schema predefined needed
read_partial_without_schema_predefined.go	read sub-struct from a parquet file and no struct/schema predefined needed
json_schema.go	define schema using json string
json_write.go	convert json to parquet
convert_to_json.go	convert parquet to json
csv_write.go	special csv writer
column_read.go	read raw column data and return value,repetitionLevel,definitionLevel
type.go	example for schema of types

Tool

• parquet-tools: Command line tools that aid in the inspection of Parquet files

Please start to use it and give feedback or start it! Help is needed and anything is welcome.