JarCastingPyJava
This library is an ongoing effort towards bringing the data exchanging ability between Java/Scala and Python. PyJava introduces Apache Arrow as the exchanging data format, this means we can avoid ser/der between Java/Scala and Python which can really speed up the communication efficiency than traditional way.
When you invoke python code in Java/Scala side, PyJava will start some python workers automatically and send the data to python worker, and once they are processed, send them back. The python workers are reused
by default.
The initial code in this lib is from Apache Spark.
Install
Setup python(>= 3.6) Env(Conda is recommended):
pip uninstall pyjava && pip install pyjava
Setup Java env(Maven is recommended):
<dependency>
<groupId>tech.mlsql</groupId>
<artifactId>pyjava-2.4_2.12</artifactId>
<version>0.2.8.0</version>
</dependency>
Using python code snippet to process data in Java/Scala
With pyjava, you can run any python code in your Java/Scala application.
val envs = new util.HashMap[String, String]()
// prepare python environment
envs.put(str(PythonConf.PYTHON_ENV), "source activate dev && export ARROW_PRE_0_15_IPC_FORMAT=1 ")
// describe the data which will be transfered to python
val sourceSchema = StructType(Seq(StructField("value", StringType)))
val batch = new ArrowPythonRunner(
Seq(ChainedPythonFunctions(Seq(PythonFunction(
"""
|import pandas as pd
|import numpy as np
|
|def process():
| for item in context.fetch_once_as_rows():
| item["value1"] = item["value"] + "_suffix"
| yield item
|
|context.build_result(process())
""".stripMargin, envs, "python", "3.6")))), sourceSchema,
"GMT", Map()
)
// prepare data
val sourceEnconder = RowEncoder.apply(sourceSchema).resolveAndBind()
val newIter = Seq(Row.fromSeq(Seq("a1")), Row.fromSeq(Seq("a2"))).map { irow =>
sourceEnconder.toRow(irow).copy()
}.iterator
// run the code and get the return result
val javaConext = new JavaContext
val commonTaskContext = new AppContextImpl(javaConext, batch)
val columnarBatchIter = batch.compute(Iterator(newIter), TaskContext.getPartitionId(), commonTaskContext)
//f.copy(), copy function is required
columnarBatchIter.flatMap { batch =>
batch.rowIterator.asScala
}.foreach(f => println(f.copy()))
javaConext.markComplete
javaConext.close
Using python code snippet to process data in Spark
val session = spark
import session.implicits._
val timezoneid = session.sessionState.conf.sessionLocalTimeZone
val df = session.createDataset[String](Seq("a1", "b1")).toDF("value")
val struct = df.schema
val abc = df.rdd.mapPartitions { iter =>
val enconder = RowEncoder.apply(struct).resolveAndBind()
val envs = new util.HashMap[String, String]()
envs.put(str(PythonConf.PYTHON_ENV), "source activate streamingpro-spark-2.4.x")
val batch = new ArrowPythonRunner(
Seq(ChainedPythonFunctions(Seq(PythonFunction(
"""
|import pandas as pd
|import numpy as np
|for item in data_manager.fetch_once():
| print(item)
|df = pd.DataFrame({'AAA': [4, 5, 6, 7],'BBB': [10, 20, 30, 40],'CCC': [100, 50, -30, -50]})
|data_manager.set_output([[df['AAA'],df['BBB']]])
""".stripMargin, envs, "python", "3.6")))), struct,
timezoneid, Map()
)
val newIter = iter.map { irow =>
enconder.toRow(irow)
}
val commonTaskContext = new SparkContextImp(TaskContext.get(), batch)
val columnarBatchIter = batch.compute(Iterator(newIter), TaskContext.getPartitionId(), commonTaskContext)
columnarBatchIter.flatMap { batch =>
batch.rowIterator.asScala.map(_.copy)
}
}
val wow = SparkUtils.internalCreateDataFrame(session, abc, StructType(Seq(StructField("AAA", LongType), StructField("BBB", LongType))), false)
wow.show()
Run Python Project
With Pyjava, you can tell the system where is the python project and which is then entrypoint, then you can run this project in Java/Scala.
import tech.mlsql.arrow.python.runner.PythonProjectRunner
val runner = new PythonProjectRunner("./pyjava/examples/pyproject1", Map())
val output = runner.run(Seq("bash", "-c", "source activate dev && python train.py"), Map(
"tempDataLocalPath" -> "/tmp/data",
"tempModelLocalPath" -> "/tmp/model"
))
output.foreach(println)
Example In MLSQL
None Interactive Mode:
!python env "PYTHON_ENV=source activate streamingpro-spark-2.4.x";
!python conf "schema=st(field(a,long),field(b,long))";
select 1 as a as table1;
!python on table1 '''
import pandas as pd
import numpy as np
for item in data_manager.fetch_once():
print(item)
df = pd.DataFrame({'AAA': [4, 5, 6, 8],'BBB': [10, 20, 30, 40],'CCC': [100, 50, -30, -50]})
data_manager.set_output([[df['AAA'],df['BBB']]])
''' named mlsql_temp_table2;
select * from mlsql_temp_table2 as output;
Interactive Mode:
!python start;
!python env "PYTHON_ENV=source activate streamingpro-spark-2.4.x";
!python env "schema=st(field(a,integer),field(b,integer))";
!python '''
import pandas as pd
import numpy as np
''';
!python '''
for item in data_manager.fetch_once():
print(item)
df = pd.DataFrame({'AAA': [4, 5, 6, 8],'BBB': [10, 20, 30, 40],'CCC': [100, 50, -30, -50]})
data_manager.set_output([[df['AAA'],df['BBB']]])
''';
!python close;
Using PyJava as Arrow Server/Client
Java Server side:
val socketRunner = new SparkSocketRunner("wow", NetUtils.getHost, "Asia/Harbin")
val dataSchema = StructType(Seq(StructField("value", StringType)))
val enconder = RowEncoder.apply(dataSchema).resolveAndBind()
val newIter = Seq(Row.fromSeq(Seq("a1")), Row.fromSeq(Seq("a2"))).map { irow =>
enconder.toRow(irow)
}.iterator
val javaConext = new JavaContext
val commonTaskContext = new AppContextImpl(javaConext, null)
val Array(_, host, port) = socketRunner.serveToStreamWithArrow(newIter, dataSchema, 10, commonTaskContext)
println(s"${host}:${port}")
Thread.currentThread().join()
Python Client side:
import os
import socket
from pyjava.serializers import \
ArrowStreamPandasSerializer
out_ser = ArrowStreamPandasSerializer(None, True, True)
out_ser = ArrowStreamPandasSerializer("Asia/Harbin", False, None)
HOST = ""
PORT = -1
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
sock.connect((HOST, PORT))
buffer_size = int(os.environ.get("SPARK_BUFFER_SIZE", 65536))
infile = os.fdopen(os.dup(sock.fileno()), "rb", buffer_size)
outfile = os.fdopen(os.dup(sock.fileno()), "wb", buffer_size)
kk = out_ser.load_stream(infile)
for item in kk:
print(item)
Python Server side:
import os
import pandas as pd
os.environ["ARROW_PRE_0_15_IPC_FORMAT"] = "1"
from pyjava.api.serve import OnceServer
ddata = pd.DataFrame(data=[[1, 2, 3, 4], [2, 3, 4, 5]])
server = OnceServer("127.0.0.1", 11111, "Asia/Harbin")
server.bind()
server.serve([{'id': 9, 'label': 1}])
Java Client side:
import org.apache.spark.sql.Row
import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.types.{LongType, StringType, StructField, StructType}
import org.scalatest.{BeforeAndAfterAll, FunSuite}
import tech.mlsql.arrow.python.iapp.{AppContextImpl, JavaContext}
import tech.mlsql.arrow.python.runner.SparkSocketRunner
import tech.mlsql.common.utils.network.NetUtils
val enconder = RowEncoder.apply(StructType(Seq(StructField("a", LongType),StructField("b", LongType)))).resolveAndBind()
val socketRunner = new SparkSocketRunner("wow", NetUtils.getHost, "Asia/Harbin")
val javaConext = new JavaContext
val commonTaskContext = new AppContextImpl(javaConext, null)
val iter = socketRunner.readFromStreamWithArrow("127.0.0.1", 11111, commonTaskContext)
iter.foreach(i => println(enconder.fromRow(i.copy())))
javaConext.close