parquet-go v1.2.8

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:

go get github.com/xitongsys/parquet-go/...

Look at a few examples in example/.

go run example/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.

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"`

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. Currently local and HDFS interfaces are implemented.(It's not possible for S3, because it doesn't support random access.)

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 some columns. 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)

Read/Write Example

Following is a simple example of read/write parquet file on local disk. It can be found in example directory:

package main
import (
	"log"
	"time"

	"github.com/xitongsys/parquet-go/ParquetFile"
	"github.com/xitongsys/parquet-go/ParquetReader"
	"github.com/xitongsys/parquet-go/ParquetWriter"
	"github.com/xitongsys/parquet-go/parquet"
)

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"`
	Ignored int32   //without parquet tag and won't write
}

func main() {
	var err error
	fw, err := ParquetFile.NewLocalFileWriter("flat.parquet")
	if err != nil {
		log.Println("Can't create local file", err)
		return
	}
	//write
	pw, err := ParquetWriter.NewParquetWriter(fw, new(Student), 4)
	if err != nil {
		log.Println("Can't create parquet writer", err)
		return
	}
	pw.RowGroupSize = 128 * 1024 * 1024 //128M
	pw.CompressionType = parquet.CompressionCodec_SNAPPY
	num := 100
	for i := 0; i < num; i++ {
		stu := Student{
			Name:   "StudentName",
			Age:    int32(20 + i%5),
			Id:     int64(i),
			Weight: float32(50.0 + float32(i)*0.1),
			Sex:    bool(i%2 == 0),
			Day:    int32(time.Now().Unix() / 3600 / 24),
		}
		if err = pw.Write(stu); err != nil {
			log.Println("Write error", err)
		}
	}
	if err = pw.WriteStop(); err != nil {
		log.Println("WriteStop error", err)
		return
	}
	log.Println("Write Finished")
	fw.Close()

	///read
	fr, err := ParquetFile.NewLocalFileReader("flat.parquet")
	if err != nil {
		log.Println("Can't open file")
		return
	}
	pr, err := ParquetReader.NewParquetReader(fr, new(Student), 4)
	if err != nil {
		log.Println("Can't create parquet reader", err)
		return
	}
	num = int(pr.GetNumRows())
	for i := 0; i < num/10; i++ {
		if i%2 == 0 {
			pr.SkipRows(10) //skip 10 rows
			continue
		}
		stus := make([]Student, 10) //read 10 rows
		if err = pr.Read(&stus); err != nil {
			log.Println("Read error", err)
		}
		log.Println(stus)
	}
	pr.ReadStop()
	fr.Close()
}

Tool

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

Status

Here are a few todo items. Welcome any help!

Add more useful tools
Performance Test(Issue14)
Test in different platforms
Star it :)

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

mapuri/parquet-go

parquet-go v1.2.8

Install

Type

Tips

Encoding

PLAIN:

PLAIN_DICTIONARY:

DELTA_BINARY_PACKED:

DELTA_BYTE_ARRAY:

DELTA_LENGTH_BYTE_ARRAY:

Tips

Repetition Type

Tips

Example of Type and Encoding

ParquetFile

Writer

Reader

Tips

Schema

Tag

JSON

CSV metadata

Parallel

Read/Write Example

Tool

Status