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
Add the parquet-go library to your $GOPATH/src and install dependencies:
go get github.com/xitongsys/parquet-goThe 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.goThe local_flat.go code shows how it's easy to output structs from Go programs to Parquet files.
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 | int8 |
| INT_16 | INT32 | int16 |
| INT_32 | INT32 | int32 |
| INT_64 | INT64 | int64 |
| UINT_8 | INT32 | uint8 |
| UINT_16 | INT32 | uint16 |
| 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 |
- Although DECIMAL can be stored as INT32,INT64,FIXED_LEN_BYTE_ARRAY,BYTE_ARRAY, Currently I suggest to use FIXED_LEN_BYTE_ARRAY.
All types
All types
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
BYTE_ARRAY, UTF8
BYTE_ARRAY, UTF8
- 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.
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 |
- 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.
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 int8 `parquet:"name=int_8, type=INT_8"`
Int_16 int16 `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 uint8 `parquet:"name=uint_8, type=UINT_8"`
Uint_16 uint16 `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"`| Type | Support |
|---|---|
| CompressionCodec_UNCOMPRESSED | YES |
| CompressionCodec_SNAPPY | YES |
| CompressionCodec_GZIP | YES |
| CompressionCodec_LZO | NO |
| CompressionCodec_BROTLI | NO |
| CompressionCodec_LZ4 | NO |
| CompressionCodec_ZSTD | YES |
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).
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
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
- 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.
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.
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"`
}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"}
]
}
]
}
`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",
}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)