parquet-go

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

Examples

The example/ directory contains several examples.

The local_flat.go example creates some data and writes it out to the example/output/flat.parquet file.

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

The local_flat.go code shows how it's easy to output structs from Go programs to Parquet files.

Type

There are two types in Parquet: Primitive Type and Logical Type. Logical types are stored as primitive types.

Primitive Type

Primitive Type	Go Type
BOOLEAN	bool
INT32	int32
INT64	int64
INT96	string
FLOAT	float32
DOUBLE	float64
BYTE_ARRAY	string
FIXED_LEN_BYTE_ARRAY	string

Logical Type

Logical Type	Primitive Type	Go Type
UTF8	BYTE_ARRAY	string
INT_8	INT32	int32
INT_16	INT32	int32
INT_32	INT32	int32
INT_64	INT64	int64
UINT_8	INT32	int32
UINT_16	INT32	int32
UINT_32	INT32	int32
UINT_64	INT64	int64
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

Parquet-go supports type alias such type MyString string. But the base type must follow the table instructions.
Some type convert functions: converter.go

Encoding

PLAIN:

All types

PLAIN_DICTIONARY/RLE_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.
If the fields have many different values, please don't use PLAIN_DICTIONARY encoding. Because it will record all the different values in a map which will use a lot of memory. Actually it use a 32-bit integer to store the index. It can not used if your unique values number is larger than 32-bit.
Large array values may be duplicated as min and max values in page stats, significantly increasing file size. If stats are not useful for such a field, they can be omitted from written files by adding omitstats=true to a field tag.

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=BYTE_ARRAY, convertedtype=UTF8, encoding=PLAIN_DICTIONARY"`
	Int_8            int32   `parquet:"name=int_8, type=INT32, convertedtype=INT32, convertedtype=INT_8"`
	Int_16           int32  `parquet:"name=int_16, type=INT32, convertedtype=INT_16"`
	Int_32           int32  `parquet:"name=int_32, type=INT32, convertedtype=INT_32"`
	Int_64           int64  `parquet:"name=int_64, type=INT64, convertedtype=INT_64"`
	Uint_8           int32  `parquet:"name=uint_8, type=INT32, convertedtype=UINT_8"`
	Uint_16          int32 `parquet:"name=uint_16, type=INT32, convertedtype=UINT_16"`
	Uint_32          int32 `parquet:"name=uint_32, type=INT32, convertedtype=UINT_32"`
	Uint_64          int64 `parquet:"name=uint_64, type=INT64, convertedtype=UINT_64"`
	Date             int32  `parquet:"name=date, type=INT32, convertedtype=DATE"`
	Date2            int32  `parquet:"name=date2, type=INT32, convertedtype=DATE, logicaltype=DATE"`
	TimeMillis       int32  `parquet:"name=timemillis, type=INT32, convertedtype=TIME_MILLIS"`
	TimeMillis2      int32  `parquet:"name=timemillis2, type=INT32, logicaltype=TIME, logicaltype.isadjustedtoutc=true, logicaltype.unit=MILLIS"`
	TimeMicros       int64  `parquet:"name=timemicros, type=INT64, convertedtype=TIME_MICROS"`
	TimeMicros2      int64  `parquet:"name=timemicros2, type=INT64, logicaltype=TIME, logicaltype.isadjustedtoutc=false, logicaltype.unit=MICROS"`
	TimestampMillis  int64  `parquet:"name=timestampmillis, type=INT64, convertedtype=TIMESTAMP_MILLIS"`
	TimestampMillis2 int64  `parquet:"name=timestampmillis2, type=INT64, logicaltype=TIMESTAMP, logicaltype.isadjustedtoutc=true, logicaltype.unit=MILLIS"`
	TimestampMicros  int64  `parquet:"name=timestampmicros, type=INT64, convertedtype=TIMESTAMP_MICROS"`
	TimestampMicros2 int64  `parquet:"name=timestampmicros2, type=INT64, logicaltype=TIMESTAMP, logicaltype.isadjustedtoutc=false, logicaltype.unit=MICROS"`
	Interval         string `parquet:"name=interval, type=BYTE_ARRAY, convertedtype=INTERVAL"`

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

	Decimal5 int32 `parquet:"name=decimal5, type=INT32, logicaltype=DECIMAL, logicaltype.precision=10, logicaltype.scale=2"`

	Map      map[string]int32 `parquet:"name=map, type=MAP, convertedtype=MAP, keytype=BYTE_ARRAY, keyconvertedtype=UTF8, valuetype=INT32"`
	List     []string         `parquet:"name=list, type=MAP, convertedtype=LIST, valuetype=BYTE_ARRAY, valueconvertedtype=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.
RowGroupSize and PageSize may influence the final parquet file size. You can find the details from here. You can reset them in ParquetWriter

	pw.RowGroupSize = 128 * 1024 * 1024 // default 128M
	pw.PageSize = 8 * 1024 // default 8K

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=BYTE_ARRAY, convertedtype=UTF8, encoding=PLAIN_DICTIONARY"`
	Age     int32   `parquet:"name=age, type=INT32, encoding=PLAIN"`
	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=INT32, convertedtype=DATE"`
	Ignored int32   //without parquet tag and won't write
}

Example of tags

JSON

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

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

	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=NameIn, type=BYTE_ARRAY, convertedtype=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=BYTE_ARRAY, convertedtype=UTF8, repetitiontype=REQUIRED"}]
    },

    {
      "Tag": "name=scores, inname=Scores, type=MAP, repetitiontype=REQUIRED",
      "Fields": [
        {"Tag": "name=key, type=BYTE_ARRAY, convertedtype=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=BYTE_ARRAY, convertedtype=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=BYTE_ARRAY, convertedtype=UTF8, repetitiontype=REQUIRED"},
        {"Tag": "name=id, inname=Id, type=INT64, repetitiontype=REQUIRED"}
      ]
    }
  ]
}
`

Example of JSON schema

CSV metadata

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

Example of CSV metadata

Tips

Parquet-go reads data as an object in Golang and every field must be a public field, which start with an upper letter. This field name we call it InName. Field name in parquet file we call it ExName. Function common.HeadToUpper converts ExName to InName. There are some restriction:

It's not allowed if two field names are only different at their first letter case. Such as name and Name.
PARGO_PREFIX_ is a reserved string, which you'd better not use it as a name prefix. (#294)
Use \x01 as the delimiter of fields to support . in some field name.(dot_in_name.go, #349)

Concurrency

Marshal/Unmarshal is the most time consuming process in writing/reading. To improve the performance, parquet-go can use multiple goroutines to marshal/unmarshal the objects. You can set the concurrent number parameter np in the Read/Write initial functions.

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
type_alias.go	example for type alias
writer.go	create ParquetWriter from io.Writer
keyvalue_metadata.go	write keyvalue metadata
dot_in_name.go	`.` in filed name

Tool

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

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

pedrohff/parquet-go

parquet-go

Install

Examples

Type

Primitive Type

Logical Type

Tips

Encoding

PLAIN:

PLAIN_DICTIONARY/RLE_DICTIONARY:

DELTA_BINARY_PACKED:

DELTA_BYTE_ARRAY:

DELTA_LENGTH_BYTE_ARRAY:

Tips

Repetition Type

Tips

Example of Type and Encoding

Compression Type

ParquetFile

Writer

Reader

Tips

Schema

Tag

JSON

CSV metadata

Tips

Concurrency

Examples

Tool