Source code for pyspark.sql.protobuf.functions

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"""
A collections of builtin protobuf functions
"""


from typing import Dict, Optional, TYPE_CHECKING, cast

from pyspark.sql.column import Column
from pyspark.sql.utils import get_active_spark_context, try_remote_protobuf_functions
from pyspark.util import _print_missing_jar

if TYPE_CHECKING:
    from pyspark.sql._typing import ColumnOrName


[docs]@try_remote_protobuf_functions def from_protobuf( data: "ColumnOrName", messageName: str, descFilePath: Optional[str] = None, options: Optional[Dict[str, str]] = None, binaryDescriptorSet: Optional[bytes] = None, ) -> Column: """ Converts a binary column of Protobuf format into its corresponding catalyst value. The Protobuf definition is provided in one of these ways: - Protobuf descriptor file: E.g. a descriptor file created with `protoc --include_imports --descriptor_set_out=abc.desc abc.proto` - Protobuf descriptor as binary: Rather than file path as in previous option, we can provide the binary content of the file. This allows flexibility in how the descriptor set is created and fetched. - Jar containing Protobuf Java class: The jar containing Java class should be shaded. Specifically, `com.google.protobuf.*` should be shaded to `org.sparkproject.spark_protobuf.protobuf.*`. https://github.com/rangadi/shaded-protobuf-classes is useful to create shaded jar from Protobuf files. The jar file can be added with spark-submit option --jars. .. versionadded:: 3.4.0 .. versionchanged:: 3.5.0 Supports `binaryDescriptorSet` arg to pass binary descriptor directly. Supports Spark Connect. Parameters ---------- data : :class:`~pyspark.sql.Column` or str the binary column. messageName: str, optional the protobuf message name to look for in descriptor file, or The Protobuf class name when descFilePath parameter is not set. E.g. `com.example.protos.ExampleEvent`. descFilePath : str, optional The Protobuf descriptor file. options : dict, optional options to control how the protobuf record is parsed. binaryDescriptorSet: bytes, optional The Protobuf `FileDescriptorSet` serialized as binary. Notes ----- Protobuf functionality is provided as an pluggable external module. Examples -------- >>> import tempfile >>> data = [("1", (2, "Alice", 109200))] >>> ddl_schema = "key STRING, value STRUCT<age: INTEGER, name: STRING, score: LONG>" >>> df = spark.createDataFrame(data, ddl_schema) >>> desc_hex = str('0ACE010A41636F6E6E6563746F722F70726F746F6275662F7372632F746573742F726' ... '5736F75726365732F70726F746F6275662F7079737061726B5F746573742E70726F746F121D6F72672E61' ... '70616368652E737061726B2E73716C2E70726F746F627566224B0A0D53696D706C654D657373616765121' ... '00A03616765180120012805520361676512120A046E616D6518022001280952046E616D6512140A057363' ... '6F7265180320012803520573636F72654215421353696D706C654D65737361676550726F746F736206707' ... '26F746F33') >>> # Writing a protobuf description into a file, generated by using >>> # connector/protobuf/src/test/resources/protobuf/pyspark_test.proto file >>> with tempfile.TemporaryDirectory(prefix="from_protobuf") as tmp_dir: ... desc_file_path = "%s/pyspark_test.desc" % tmp_dir ... with open(desc_file_path, "wb") as f: ... _ = f.write(bytearray.fromhex(desc_hex)) ... f.flush() ... message_name = 'SimpleMessage' ... proto_df = df.select( ... to_protobuf(df.value, message_name, desc_file_path).alias("value")) ... proto_df.show(truncate=False) ... proto_df_1 = proto_df.select( # With file name for descriptor ... from_protobuf(proto_df.value, message_name, desc_file_path).alias("value")) ... proto_df_1.show(truncate=False) ... proto_df_2 = proto_df.select( # With binary for descriptor ... from_protobuf(proto_df.value, message_name, ... binaryDescriptorSet = bytearray.fromhex(desc_hex)) ... .alias("value")) ... proto_df_2.show(truncate=False) +----------------------------------------+ |value | +----------------------------------------+ |[08 02 12 05 41 6C 69 63 65 18 90 D5 06]| +----------------------------------------+ +------------------+ |value | +------------------+ |{2, Alice, 109200}| +------------------+ +------------------+ |value | +------------------+ |{2, Alice, 109200}| +------------------+ >>> data = [([(1668035962, 2020)])] >>> ddl_schema = "value struct<seconds: LONG, nanos: INT>" >>> df = spark.createDataFrame(data, ddl_schema) >>> message_class_name = "org.sparkproject.spark_protobuf.protobuf.Timestamp" >>> to_proto_df = df.select(to_protobuf(df.value, message_class_name).alias("value")) >>> from_proto_df = to_proto_df.select( ... from_protobuf(to_proto_df.value, message_class_name).alias("value")) >>> from_proto_df.show(truncate=False) +------------------+ |value | +------------------+ |{1668035962, 2020}| +------------------+ """ from py4j.java_gateway import JVMView from pyspark.sql.classic.column import _to_java_column sc = get_active_spark_context() try: binary_proto = None if binaryDescriptorSet is not None: binary_proto = binaryDescriptorSet elif descFilePath is not None: binary_proto = _read_descriptor_set_file(descFilePath) if binary_proto is not None: jc = cast(JVMView, sc._jvm).org.apache.spark.sql.protobuf.functions.from_protobuf( _to_java_column(data), messageName, binary_proto, options or {} ) else: jc = cast(JVMView, sc._jvm).org.apache.spark.sql.protobuf.functions.from_protobuf( _to_java_column(data), messageName, options or {} ) except TypeError as e: if str(e) == "'JavaPackage' object is not callable": _print_missing_jar("Protobuf", "protobuf", "protobuf", sc.version) raise return Column(jc)
[docs]@try_remote_protobuf_functions def to_protobuf( data: "ColumnOrName", messageName: str, descFilePath: Optional[str] = None, options: Optional[Dict[str, str]] = None, binaryDescriptorSet: Optional[bytes] = None, ) -> Column: """ Converts a column into binary of protobuf format. The Protobuf definition is provided in one of these ways: - Protobuf descriptor file: E.g. a descriptor file created with `protoc --include_imports --descriptor_set_out=abc.desc abc.proto` - Protobuf descriptor as binary: Rather than file path as in previous option, we can provide the binary content of the file. This allows flexibility in how the descriptor set is created and fetched. - Jar containing Protobuf Java class: The jar containing Java class should be shaded. Specifically, `com.google.protobuf.*` should be shaded to `org.sparkproject.spark_protobuf.protobuf.*`. https://github.com/rangadi/shaded-protobuf-classes is useful to create shaded jar from Protobuf files. The jar file can be added with spark-submit option --jars. .. versionadded:: 3.4.0 .. versionchanged:: 3.5.0 Supports `binaryDescriptorSet` arg to pass binary descriptor directly. Supports Spark Connect. Parameters ---------- data : :class:`~pyspark.sql.Column` or str the data column. messageName: str, optional the protobuf message name to look for in descriptor file, or The Protobuf class name when descFilePath parameter is not set. E.g. `com.example.protos.ExampleEvent`. descFilePath : str, optional the Protobuf descriptor file. options : dict, optional binaryDescriptorSet: bytes, optional The Protobuf `FileDescriptorSet` serialized as binary. Notes ----- Protobuf functionality is provided as a pluggable external module Examples -------- >>> import tempfile >>> data = [([(2, "Alice", 13093020)])] >>> ddl_schema = "value struct<age: INTEGER, name: STRING, score: LONG>" >>> df = spark.createDataFrame(data, ddl_schema) >>> desc_hex = str('0ACE010A41636F6E6E6563746F722F70726F746F6275662F7372632F746573742F726' ... '5736F75726365732F70726F746F6275662F7079737061726B5F746573742E70726F746F121D6F72672E61' ... '70616368652E737061726B2E73716C2E70726F746F627566224B0A0D53696D706C654D657373616765121' ... '00A03616765180120012805520361676512120A046E616D6518022001280952046E616D6512140A057363' ... '6F7265180320012803520573636F72654215421353696D706C654D65737361676550726F746F736206707' ... '26F746F33') >>> # Writing a protobuf description into a file, generated by using >>> # connector/protobuf/src/test/resources/protobuf/pyspark_test.proto file >>> with tempfile.TemporaryDirectory(prefix="to_protobuf") as tmp_dir: ... desc_file_path = "%s/pyspark_test.desc" % tmp_dir ... with open(desc_file_path, "wb") as f: ... _ = f.write(bytearray.fromhex(desc_hex)) ... f.flush() ... message_name = 'SimpleMessage' ... proto_df = df.select( # With file name for descriptor ... to_protobuf(df.value, message_name, desc_file_path).alias("suite")) ... proto_df.show(truncate=False) ... proto_df_2 = df.select( # With binary for descriptor ... to_protobuf(df.value, message_name, ... binaryDescriptorSet=bytearray.fromhex(desc_hex)) ... .alias("suite")) ... proto_df_2.show(truncate=False) +-------------------------------------------+ |suite | +-------------------------------------------+ |[08 02 12 05 41 6C 69 63 65 18 9C 91 9F 06]| +-------------------------------------------+ +-------------------------------------------+ |suite | +-------------------------------------------+ |[08 02 12 05 41 6C 69 63 65 18 9C 91 9F 06]| +-------------------------------------------+ >>> data = [([(1668035962, 2020)])] >>> ddl_schema = "value struct<seconds: LONG, nanos: INT>" >>> df = spark.createDataFrame(data, ddl_schema) >>> message_class_name = "org.sparkproject.spark_protobuf.protobuf.Timestamp" >>> proto_df = df.select(to_protobuf(df.value, message_class_name).alias("suite")) >>> proto_df.show(truncate=False) +----------------------------+ |suite | +----------------------------+ |[08 FA EA B0 9B 06 10 E4 0F]| +----------------------------+ """ from py4j.java_gateway import JVMView from pyspark.sql.classic.column import _to_java_column sc = get_active_spark_context() try: binary_proto = None if binaryDescriptorSet is not None: binary_proto = binaryDescriptorSet elif descFilePath is not None: binary_proto = _read_descriptor_set_file(descFilePath) if binary_proto is not None: jc = cast(JVMView, sc._jvm).org.apache.spark.sql.protobuf.functions.to_protobuf( _to_java_column(data), messageName, binary_proto, options or {} ) else: jc = cast(JVMView, sc._jvm).org.apache.spark.sql.protobuf.functions.to_protobuf( _to_java_column(data), messageName, options or {} ) except TypeError as e: if str(e) == "'JavaPackage' object is not callable": _print_missing_jar("Protobuf", "protobuf", "protobuf", sc.version) raise return Column(jc)
def _read_descriptor_set_file(filePath: str) -> bytes: # TODO(SPARK-43847): Throw structured errors like "PROTOBUF_DESCRIPTOR_FILE_NOT_FOUND" etc. with open(filePath, "rb") as f: return f.read() def _test() -> None: import os import sys from pyspark.testing.utils import search_jar protobuf_jar = search_jar("connector/protobuf", "spark-protobuf-assembly-", "spark-protobuf") if protobuf_jar is None: print( "Skipping all Protobuf Python tests as the optional Protobuf project was " "not compiled into a JAR. To run these tests, " "you need to build Spark with 'build/sbt package' or " "'build/mvn package' before running this test." ) sys.exit(0) else: existing_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell") jars_args = "--jars %s" % protobuf_jar os.environ["PYSPARK_SUBMIT_ARGS"] = " ".join([jars_args, existing_args]) import doctest from pyspark.sql import SparkSession import pyspark.sql.protobuf.functions globs = pyspark.sql.protobuf.functions.__dict__.copy() spark = ( SparkSession.builder.master("local[2]") .appName("sql.protobuf.functions tests") .getOrCreate() ) globs["spark"] = spark (failure_count, test_count) = doctest.testmod( pyspark.sql.protobuf.functions, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE, ) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()