JarCastingParquet4S
Simple I/O for Parquet. Allows you to easily read and write Parquet files in Scala.
Use just a Scala case class to define the schema of your data. No need to use Avro, Protobuf, Thrift or other data serialisation systems. You can use generic records if you don't want to use the case class, too.
Compatible with files generated with Apache Spark. However, unlike in Spark, you do not have to start a cluster to perform I/O operations.
Based on official Parquet library, Hadoop Client and Shapeless.
Integrations for Akka Streams and FS2.
Released for Scala 2.11.x, 2.12.x and 2.13.x. FS2 integration is available for 2.12.x and 2.13.x.
Tutorial
- Quick Start
- AWS S3
- Akka Streams
- FS2
- Before-read filtering or filter pushdown
- Schema projection
- Supported storage types
- Supported types
- Generic Records
- Customisation and Extensibility
- More Examples
- Contributing
Quick Start
SBT
libraryDependencies ++= Seq(
"com.github.mjakubowski84" %% "parquet4s-core" % "1.7.0",
"org.apache.hadoop" % "hadoop-client" % yourHadoopVersion
)
Mill
def ivyDeps = Agg(
ivy"com.github.mjakubowski84::parquet4s-core:1.7.0",
ivy"org.apache.hadoop:hadoop-client:$yourHadoopVersion"
)
import com.github.mjakubowski84.parquet4s.{ ParquetReader, ParquetWriter }
case class User(userId: String, name: String, created: java.sql.Timestamp)
val users: Iterable[User] = Seq(
User("1", "parquet", new java.sql.Timestamp(1L))
)
val path = "path/to/local/parquet"
// writing
ParquetWriter.writeAndClose(path, users)
// reading
val parquetIterable = ParquetReader.read[User](path)
try {
parquetIterable.foreach(println)
} finally parquetIterable.close()
AWS S3
In order to connect to AWS S3 you need to define one more dependency:
"org.apache.hadoop" % "hadoop-aws" % yourHadoopVersion
Next, the most common way is to define following environmental variables:
export AWS_ACCESS_KEY_ID=my.aws.key
export AWS_SECRET_ACCESS_KEY=my.secret.key
Please follow documentation of Hadoop AWS for more details and troubleshooting.
Passing Hadoop Configs Programmatically
File system configs for S3, GCS or Hadoop can also be set programmatically to the ParquetReader and ParquetWriter by passing the Configuration object to the ParqetReader.Options and ParquetWriter.Options case classes.
Akka Streams
Parquet4S has an integration module that allows you to read and write Parquet files using Akka Streams. Just import:
"com.github.mjakubowski84" %% "parquet4s-akka" % "1.7.0"
"org.apache.hadoop" % "hadoop-client" % yourHadoopVersion
Parquet4S has so far a single Source for reading single file or directory and Sinks for writing.
import com.github.mjakubowski84.parquet4s.{ParquetStreams, ParquetWriter}
import org.apache.parquet.hadoop.ParquetFileWriter
import org.apache.parquet.hadoop.metadata.CompressionCodecName
import akka.actor.ActorSystem
import akka.stream.{ActorMaterializer, Materializer}
import akka.stream.scaladsl.Source
import org.apache.hadoop.conf.Configuration
import scala.concurrent.duration._
case class User(userId: String, name: String, created: java.sql.Timestamp)
implicit val system: ActorSystem = ActorSystem()
implicit val materializer: Materializer = ActorMaterializer()
val users: Iterable[User] = ???
val conf: Configuration = ??? // Set Hadoop configuration programmatically
// Please check all the available configuration options!
val writeOptions = ParquetWriter.Options(
writeMode = ParquetFileWriter.Mode.OVERWRITE,
compressionCodecName = CompressionCodecName.SNAPPY,
hadoopConf = conf // optional hadoopConf
)
// Writes a single file.
Source(users).runWith(ParquetStreams.toParquetSingleFile(
path = "file:///data/users/user-303.parquet",
options = writeOptions
))
// Tailored for writing indefinite streams.
// Writes file when chunk reaches size limit or defined time period elapses.
// Can also partition files!
// Check all the parameters and example usage in project sources.
Source(users).via(
ParquetStreams
.viaParquet[User]("file:///data/users")
.withMaxCount(writeOptions.rowGroupSize)
.withMaxDuration(30.seconds)
.withWriteOptions(writeOptions)
.build()
).runForeach(user => println(s"Just wrote user ${user.userId}..."))
// Reads a file or files from the path. Please also have a look at the rest of parameters.
ParquetStreams.fromParquet[User]
.withOptions(ParquetReader.Options(hadoopConf = conf))
.read("file:///data/users")
.runForeach(println)
FS2
FS2 integration allows you to read and write Parquet using functional streams. In order to use it please import:
"com.github.mjakubowski84" %% "parquet4s-fs2" % "1.7.0"
"org.apache.hadoop" % "hadoop-client" % yourHadoopVersion
Please check examples to learn more.
Before-read filtering or filter pushdown
One of the best features of Parquet is an efficient way of fitering. Parquet files contain additional metadata that can be leveraged to drop chunks of data without scanning them. Parquet4S allows do define filter predicates in all modules in order to push filtering out from Scala collections and Akka or FS2 stream down to point before file content is even read.
You define you filters using simple algebra as follows.
In core library:
ParquetReader.read[User](path = "file://my/path", filter = Col("email") === "[email protected]")
In Akka filter applies both to content of files and partitions:
ParquetStreams.fromParquet[Stats]
.withFilter(Col("stats.score") > 0.9 && Col("stats.score") <= 1.0)
.read("file://my/path")
You can construct filter predicates using ===, !==, >, >=, <, <=, and in operators on columns containing primitive values. You can combine and modify predicates using &&, || and ! operators. in looks for values in a list of keys, similar to SQL's in operator. Mind that operations on java.sql.Timestamp and java.time.LocalDateTime are not supported as Parquet still not allows filtering by Int96 out of the box.
Check ScalaDoc and code for more!
Schema projection
Schema projection is another way of optimization of reads. By default Parquet4S reads the whole content of each Parquet record even when you provide a case class that maps only a part of the columns. Such a behaviour is expected because you may want to use generic records to process your data. However, you can explicitely tell Parquet4S to use the provided case class (or implicit ParquetSchemaResolver) as an override for the original file schema. In effect, all columns not matching your schema will be skipped and not read. This functionality is available in every module of Parquet4S.
// core
ParquetReader.withProjection[User].read(path = "file://my/path")
// akka
ParquetStreams.fromParquet[User].withProjection.read("file://my/path")
// fs2
import com.github.mjakubowski84.parquet4s.parquet._
fromParquet[IO, User].projection.read(blocker, "file://my/path")
Supported storage types
As it is based on Hadoop Client, Parquet4S can read and write from a variety of file systems:
- Local files
- HDFS
- Amazon S3
- Google Storage
- Azure
- OpenStack
Please refer to Hadoop Client documentation or your storage provider to check how to connect to your storage.
Supported types
Primitive types
| Type | Reading and Writing | Filtering |
|---|---|---|
| Int | |
|
| Long | |
|
| Byte | |
|
| Short | |
|
| Boolean | |
|
| Char | |
|
| Float | |
|
| Double | |
|
| BigDecimal | |
|
| java.time.LocalDateTime | |
☒ |
| java.time.LocalDate | |
|
| java.sql.Timestamp | |
☒ |
| java.sql.Date | |
|
| Array[Byte] | |
|
Complex Types
Complex types can be arbitrarily nested.
- Option
- List
- Seq
- Vector
- Set
- Array - Array of bytes is treated as primitive binary
- Map - Key must be of primitive type, only immutable version.
- Since 1.2.0. Any Scala collection that has Scala 2.13 collection Factory (in 2.11 and 2.12 it is derived from CanBuildFrom). Refers to both mutable and immutable collections. Collection must be bounded only by one type of element - because of that Map is supported only in immutable version (for now).
- Any case class
Generic Records
You may want to not use strict schema and process your data in a generic way. Since version 1.2.0 Parquet4S has rich API that allows to build, transform, write and read Parquet records in easy way. Each implementation of ParquetRecord is Scala Iterable and a mutable collection. You can execute operations on RowParquetRecord and ListParquetRecord as on mutable Seq and you can treat MapParquetRecord as mutable Map. Moreover, records received addition functions like get and add (and more) that take implicit ValueCodec and allow to read and modify records using regular Scala types. There is default ParquetRecordEndcoder, ParquetRecordDecoder and ParquetSchemaResolver for RowParquetRecord so reading Parquet in a generic way works out of the box! In order to write you still need to provide a schema in form of Parquet's MessageType.
Funcionality is available in all modules. See examples.
Customisation and Extensibility
Parquet4S is built using Scala's type class system. That allows you to extend Parquet4S by defining your own implementations of its type classes.
For example, you may define your codecs of your own type so that they can be read from or written to Parquet. Assume that you have your own type:
case class CustomType(i: Int)
You want to save it as optional Int. In order to achieve you have to define your own codec:
import com.github.mjakubowski84.parquet4s.{OptionalValueCodec, IntValue, Value}
implicit val customTypeCodec: OptionalValueCodec[CustomType] =
new OptionalValueCodec[CustomType] {
override protected def decodeNonNull(value: Value, configuration: ValueCodecConfiguration): CustomType = value match {
case IntValue(i) => CustomType(i)
}
override protected def encodeNonNull(data: CustomType, configuration: ValueCodecConfiguration): Value =
IntValue(data.i)
}
Additionally, if you want to write your custom type, you have to define the schema for it:
import org.apache.parquet.schema.{OriginalType, PrimitiveType}
import com.github.mjakubowski84.parquet4s.ParquetSchemaResolver._
implicit val customTypeSchema: TypedSchemaDef[CustomType] =
typedSchemaDef[CustomType](
PrimitiveSchemaDef(
primitiveType = PrimitiveType.PrimitiveTypeName.INT32,
required = false,
originalType = Some(OriginalType.INT_32)
)
)
More Examples
Please check examples where you can find simple code covering basics for core, akka and fs2 modules.
Moreover, examples contain two simple applications comprising Akka Streams or FS2 and Kafka. It shows how you can write partitioned Parquet files with data coming from an indefinite stream.
Contributing
Do you want to contribute? Please read the contribution guidelines.