Pain free Spark/Cobol files integration.
Seamlessly query your COBOL/EBCDIC binary files as Spark Dataframes and streams.
Add mainframe as a source to your data engineering strategy.
Among the motivations for this project, it is possible to highlight:
-
Lack of expertise in the Cobol ecosystem, which makes it hard to integrate mainframes into data engineering strategies
-
Lack of support from the open-source community to initiatives in this field
-
The overwhelming majority (if not all) of tools to cope with this domain are proprietary
-
Several institutions struggle daily to maintain their legacy mainframes, which prevents them from evolving to more modern approaches to data management
-
Mainframe data can only take part in data science activities through very expensive investments
-
Supports primitive types (although some are "Cobol compiler specific")
-
Supports REDEFINES, OCCURS and DEPENDING ON fields (e.g. unchecked unions and variable-size arrays)
-
Supports nested structures and arrays
-
Supports HDFS as well as local file systems
-
The COBOL copybooks parser doesn't have a Spark dependency and can be reused for integrating into other data processing engines
We have presented Cobrix at DataWorks Summit 2019 and Spark Summit 2019 conferences. The screencasts are available here:
DataWorks Summit 2019 (General Cobrix workflow for hierarchical databases): https://www.youtube.com/watch?v=o_up7X3ZL24
Spark Summit 2019 (More detailed overview of performance optimizations): https://www.youtube.com/watch?v=BOBIdGf3Tm0
| spark-cobol | Spark |
|---|---|
| 0.x | 2.2+ |
| 1.x | 2.2+ |
| 2.x | 2.4.3+ |
| 2.6.x+ | 3.2.0+ |
You can link against this library in your program at the following coordinates:
| Scala 2.11 | Scala 2.12 | Scala 2.13 |
|---|---|---|
 |
 |
 |
groupId: za.co.absa.cobrix |
groupId: za.co.absa.cobrix |
groupId: za.co.absa.cobrix |
This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell:
$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.11:2.7.8
$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.12:2.7.8
$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.13:2.7.8
This repository contains several standalone example applications in examples/spark-cobol-app directory.
It is a Maven project that contains several examples:
SparkTypesAppis an example of a very simple mainframe file processing. It is a fixed record length raw data file with a corresponding copybook. The copybook contains examples of various numeric data types Cobrix supports.SparkCobolAppis an example of a Spark Job for handling multisegment variable record length mainframe files.SparkCodecAppis an example usage of a custom record header parser. This application reads a variable record length file having non-standard RDW headers. In this example RDH header is 5 bytes instead of 4SparkCobolHierarchicalis an example processing of an EBCDIC multisegment file extracted from a hierarchical database.
The example project can be used as a template for creating Spark Application. Refer to README.md of that project for the detailed guide how to run the examples locally and on a cluster.
When running mvn clean package in examples/spark-cobol-app an uber jar will be created. It can be used to run
jobs via spark-submit or spark-shell.
sbt ++{scala_version} jacocoCode coverage will be generated on path:
{project-root}/cobrix/{module}/target/scala-{scala_version}/jacoco/report/html
-
Create a Spark
SQLContext -
Start a
sqlContext.readoperation specifyingza.co.absa.cobrix.spark.cobol.sourceas the format -
Inform the path to the copybook describing the files through
... .option("copybook", "path_to_copybook_file"). By default the copybook is expected to be in HDFS. You can specify that a copybook is located in the local file system by addingfile://prefix. For example, you can specify a local file like this.option("copybook", "file:///home/user/data/compybook.cpy"). Alternatively, instead of providing a path to a copybook file you can provide the contents of the copybook itself by using.option("copybook_contents", "...copybook contents..."). -
Inform the path to the HDFS directory containing the files:
... .load("path_to_directory_containing_the_binary_files") -
Inform the query you would like to run on the Cobol Dataframe
Below is an example whose full version can be found at za.co.absa.cobrix.spark.cobol.examples.SampleApp and za.co.absa.cobrix.spark.cobol.examples.CobolSparkExample
val sparkBuilder = SparkSession.builder().appName("Example")
val spark = sparkBuilder
.getOrCreate()
val cobolDataframe = spark
.read
.format("cobol")
.option("copybook", "data/test1_copybook.cob")
.load("data/test2_data")
cobolDataframe
.filter("RECORD.ID % 2 = 0") // filter the even values of the nested field 'RECORD_LENGTH'
.take(10)
.foreach(v => println(v))The full example is available here
In some scenarios Spark is unable to find "cobol" data source by it's short name. In that case you can use the full path to the source class instead: .format("za.co.absa.cobrix.spark.cobol.source")
Cobrix assumes input data is encoded in EBCDIC. You can load ASCII files as well by specifying the following option:
.option("encoding", "ascii").
If the input file is a text file (CRLF / LF are used to split records), use
.option("is_text", "true").
Multisegment ASCII text files are supported using this option:
.option("record_format", "D").
Cobrix has better handling of special characters and partial records using its extension format:
.option("record_format", "D2").
Read more on record formats at https://www.ibm.com/docs/en/zos/2.4.0?topic=files-selecting-record-formats-non-vsam-data-sets
-
Create a Spark
StreamContext -
Import the binary files/stream conversion manager:
za.co.absa.spark.cobol.source.streaming.CobolStreamer._ -
Read the binary files contained in the path informed in the creation of the
SparkSessionas a stream:... streamingContext.cobolStream() -
Apply queries on the stream:
... stream.filter("some_filter") ... -
Start the streaming job.
Below is an example whose full version can be found at za.co.absa.cobrix.spark.cobol.examples.StreamingExample
val spark = SparkSession
.builder()
.appName("CobolParser")
.master("local[2]")
.config("duration", 2)
.config("copybook", "path_to_the_copybook")
.config("path", "path_to_source_directory") // could be both, local or HDFS
.getOrCreate()
val streamingContext = new StreamingContext(spark.sparkContext, Seconds(3))
import za.co.absa.spark.cobol.source.streaming.CobolStreamer._ // imports the Cobol streams manager
val stream = streamingContext.cobolStream() // streams the binary files into the application
stream
.filter(row => row.getAs[Integer]("NUMERIC_FLD") % 2 == 0) // filters the even values of the nested field 'NUMERIC_FLD'
.print(10)
streamingContext.start()
streamingContext.awaitTermination()To query mainframe files interactively using spark-shell you need to provide jar(s) containing Corbrix and it's dependencies.
This can be done either by downloading all the dependencies as separate jars or by creating an uber jar that contains all
of the dependencies.
Cobrix's spark-cobol data source depends on the COBOL parser that is a part of Cobrix itself and on scodec libraries
to decode various binary formats.
The jars that you need to get are:
- spark-cobol_2.12-2.7.8.jar
- cobol-parser_2.12-2.7.8.jar
- scodec-core_2.12-1.10.3.jar
- scodec-bits_2.12-1.1.4.jar
Versions older than 2.7.1 also need
antlr4-runtime-4.8.jar.
After that you can specify these jars in spark-shell command line. Here is an example:
$ spark-shell --packages za.co.absa.cobrix:spark-cobol_2.12:2.7.8
or
$ spark-shell --master yarn --deploy-mode client --driver-cores 4 --driver-memory 4G --jars spark-cobol_2.12-2.7.8.jar,cobol-parser_2.12-2.7.8.jar,scodec-core_2.12-1.10.3.jar,scodec-bits_2.12-1.1.4.jar
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = yarn, app id = application_1535701365011_2721).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.4.5
/_/
Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_171)
Type in expressions to have them evaluated.
Type :help for more information.
scala> val df = spark.read.format("cobol").option("copybook", "/data/example1/test3_copybook.cob").load("/data/example1/data")
df: org.apache.spark.sql.DataFrame = [TRANSDATA: struct<CURRENCY: string, SIGNATURE: string ... 4 more fields>]
scala> df.show(false)
+----------------------------------------------------+
|TRANSDATA |
+----------------------------------------------------+
|[GBP,S9276511,Beierbauh.,0123330087,1,89341.00] |
|[ZAR,S9276511,Etqutsa Inc.,0039003991,1,2634633.00] |
|[USD,S9276511,Beierbauh.,0038903321,0,75.71] |
|[ZAR,S9276511,Beierbauh.,0123330087,0,215.39] |
|[ZAR,S9276511,Test Bank,0092317899,1,643.94] |
|[ZAR,S9276511,Xingzhoug,8822278911,1,998.03] |
|[USD,S9276511,Beierbauh.,0123330087,1,848.88] |
|[USD,S9276511,Beierbauh.,0123330087,0,664.11] |
|[ZAR,S9276511,Beierbauh.,0123330087,1,55262.00] |
+----------------------------------------------------+
only showing top 20 rows
scala>
Gathering all dependencies manually maybe a tiresome task. A better approach would be to create a jar file that contains all required dependencies (an uber jar aka fat jar).
Creating an uber jar for Cobrix is very easy. Steps to build:
- Install JDK 8
- Install SBT
- Clone Cobrix repository
- Run
sbt assemblyin the root directory of the repository specifying the Scala and Spark version you want to build for:# For Scala 2.11 sbt -DSPARK_VERSION="2.4.8" ++2.11.12 assembly # For Scala 2.12 sbt -DSPARK_VERSION="2.4.8" ++2.12.20 assembly sbt -DSPARK_VERSION="3.1.3" ++2.12.20 assembly sbt -DSPARK_VERSION="3.2.3" ++2.12.20 assembly sbt -DSPARK_VERSION="3.3.2" ++2.12.20 assembly sbt -DSPARK_VERSION="3.4.0" ++2.12.20 assembly # For Scala 2.13 sbt -DSPARK_VERSION="3.3.2" ++2.13.15 assembly sbt -DSPARK_VERSION="3.4.0" ++2.13.15 assembly
You can collect the uber jar of spark-cobol either at
spark-cobol/target/scala-2.11/ or in spark-cobol/target/scala-2.12/ depending on the Scala version you used.
The fat jar will have '-bundle' suffix. You can also download pre-built bundles from https://github.com/AbsaOSS/cobrix/releases/tag/v2.7.3
Then, run spark-shell or spark-submit adding the fat jar as the option.
$ spark-shell --jars spark-cobol_2.12_3.3-2.7.9-SNAPSHOT-bundle.jarA note for building and running tests on Windows
java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$POSIX.statis a Hadoop compatibility with Windows issue. The workaround is described here: https://stackoverflow.com/questions/41851066/exception-in-thread-main-java-lang-unsatisfiedlinkerror-org-apache-hadoop-io- When running assembly with
-DSPARK_VERSION=...on Windows, it seems an sbt compatibility with Windows issue: https://stackoverflow.com/questions/59144913/run-sbt-1-2-8-project-with-java-d-options-on-windows You can work around it by using default Spark version for a given Scala version:sbt ++2.11.12 assembly sbt ++2.12.20 assembly sbt ++2.13.15 assembly
Currently, specifying multiple paths in load() is not supported. Use the following syntax:
spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("paths", inputPaths.mkString(","))
.load()This library also provides convenient methods to extract Spark SQL schemas and Cobol layouts from copybooks.
If you want to extract a Spark SQL schema from a copybook by providing same options you provide to Spark:
// Same options that you use for spark.read.format("cobol").option()
val options = Map("schema_retention_policy" -> "keep_original")
val cobolSchema = CobolSchema.fromSparkOptions(Seq(copybook), options)
val sparkSchema = cobolSchema.getSparkSchema.toString()
println(sparkSchema)If you want to extract a Spark SQL schema from a copybook using the Cobol parser directly:
import za.co.absa.cobrix.cobol.parser.CopybookParser
import za.co.absa.cobrix.cobol.reader.policies.SchemaRetentionPolicy
import za.co.absa.cobrix.spark.cobol.schema.CobolSchema
val parsedSchema = CopybookParser.parseSimple(copyBookContents)
val cobolSchema = new CobolSchema(parsedSchema, SchemaRetentionPolicy.CollapseRoot, inputFileNameField = "", generateRecordId = false)
val sparkSchema = cobolSchema.getSparkSchema.toString()
println(sparkSchema)If you want to check the layout of the copybook:
import za.co.absa.cobrix.cobol.parser.CopybookParser
val copyBook = CopybookParser.parseSimple(copyBookContents)
println(copyBook.generateRecordLayoutPositions())When a copybook is converted to a Spark schema, some information is lost, such as length of string fields or minimum and maximum number of elements in arrays. To preserve this information, Cobrix adds metadata to Spark schema fields. The following metadata is added:
| Metadata key | Description |
|---|---|
| maxLength | The maximum length of a string field. |
| minElements | The minimum number of elements in an array. |
| maxElements | The maximum number of elements in an array. |
You can access the metadata in the usual way:
// This example returns the maximum length of a string field that is the first field of the copybook
df.schema.fields(0).metadata.getLong("maxLength")Cobrix assumes files has fixed length (F) record format by default. The record length is determined by the length of
the record defined by the copybook. But you can specify the record length explicitly:
.option("record_format", "F")
.option("record_length", "250")
Fixed block record formats (FB) are also supported. The support is experimental, if you find any issues, please
let us know. When the record format is 'FB' you can specify block length or number of records per
block. As with 'F' if record_length is not specified, it will be determined from the copybook.
Records that have BDWs, but not rdws can be read like this:
.option("record_format", "FB")
.option("record_length", "250")
or simply
.option("record_format", "FB")
Records that have neither BDWs nor RDWs can be read like this:
.option("record_format", "FB")
.option("record_length", "250")
.option("block_length", "500")
or
.option("record_format", "FB")
.option("record_length", "250")
.option("records_per_block", "2")
More on fixed-length record formats: https://www.ibm.com/docs/en/zos/2.3.0?topic=sets-fixed-length-record-formats
Cobrix supports variable record length files. The only requirement is that such a file should contain a standard 4 byte record header known as Record Descriptor Word (RDW). Such headers are created automatically when a variable record length file is copied from a mainframe. Another type of files are variable blocked length. Such files contain Block Descriptor Word (BDW), as well as Record Descriptor Word (RDW) headers. Any such header can be either big-endian or little-endian. Also, quite often BDW headers need to be adjusted in order to be read properly. See the use cases section below.
To load variable length record file the following option should be specified:
.option("record_format", "V")
To load variable blocked length record file the following option should be specified:
.option("record_format", "VB")
More on record formats: https://www.ibm.com/docs/en/zos/2.3.0?topic=files-selecting-record-formats-non-vsam-data-sets
The space used by the headers (both BDW and RDW) should not be mentioned in the copybook if this option is used. Please refer to the 'Record headers support' section below.
If a record of the copybook contains record lengths for each record you can use 'record_length_field' like this:
.option("record_format", "F")
.option("record_length_field", "RECORD_LENGTH")
You can use expressions as well:
.option("record_format", "F")
.option("record_length_field", "RECORD_LENGTH + 10")
or
.option("record_format", "F")
.option("record_length_field", "FIELD1 * 10 + 200")
If the record field contains a string that can be mapped to a record size, you can add the mapping as a JSON:
.option("record_format", "F")
.option("record_length_field", "FIELD_STR")
.option("record_length_map", """{"SEG1":100,"SEG2":200}""")
In order to understand the file format it is often sufficient to look at the first 4 bytes of the file (un case of RDW only files), or the first 8 bytes of a file + lookup the offset of the block (in case of BDW + RDW)
In order to determine if an RDW is a big- or little-endian, take a look at the first 4 bytes. If the first 2 bytes are zeros, it's a little-endian RDW header, otherwise it is a big-endian RDW header.
| Header example | Description | Options |
|---|---|---|
00 10 00 00 |
Big-endian RDW, no adjustments, the record size: 0x10 = 16 bytes |
.option("record_format", "V").option("is_rdw_big_endian", "true") |
01 10 00 00 |
Big-endian RDW, adjustment -2,the record size: 0x01*256 + 0x10 - 2 = 256 + 16 + 2 = 270 bytes |
.option("record_format", "V").option("is_rdw_big_endian", "true").option("rdw_adjustment", -2) |
00 00 10 00 |
Little-endian RDW, no adjustments, the record size: 0x10 = 16 bytes |
.option("record_format", "V").option("is_rdw_big_endian", "false") |
00 00 10 01 |
Little-endian RDW, adjustment -2,the record size: 0x01*256 + 0x10 - 2 = 256 + 16 + 2 = 270 bytes |
.option("record_format", "V").option("is_rdw_big_endian", "false").option("rdw_adjustment", -2) |
It is harder to determine if a BDW header is big- or little-endian since BDW header bytes can be all non-zero. But for VB format RDWs follow BDWs and endiness. You can determine the endiness from an RDW, and use the same option for BDW.
| Header example | Description | Options |
|---|---|---|
00 28 00 00 00 10 00 00 (BDW, RDW) |
Big-endian BDW+RDW, no adjustments, BDW = 0x28 = 40 byesthe record size: 0x10 = 16 bytes |
.option("record_format", "VB").option("is_bdw_big_endian", "true").option("is_rdw_big_endian", "true") |
00 2C 00 00 00 10 00 00 (BDW, RDW) |
Big-endian BDW+RDW, need -4 byte adjustment since BDW includes its own length, BDW = 0x2C - 4 = 40 byesthe record size: 0x10 = 16 bytes |
.option("record_format", "VB").option("is_bdw_big_endian", "true").option("is_rdw_big_endian", "true").option("rdw_adjustment", -4) |
00 00 28 00 00 00 10 00 (BDW, RDW) |
Little-endian BDW+RDW, no adjustments, BDW = 0x28 = 40 byesthe record size: 0x10 = 16 bytes |
.option("record_format", "VB").option("is_bdw_big_endian", "false").option("is_rdw_big_endian", "false") |
00 00 2C 00 00 00 10 00 (BDW, RDW) |
Little-endian BDW+RDW, need -4 byte adjustment since BDW includes its own length, BDW = 0x2C - 4 = 40 byesthe record size: 0x10 = 16 bytes |
.option("record_format", "VB").option("is_bdw_big_endian", "false").option("is_rdw_big_endian", "false").option("rdw_adjustment", -4) |
Mainframe data often contain only one root GROUP. In such cases such a GROUP can be considered something similar to XML rowtag. Cobrix allows either to collapse or to retain the GROUP. To turn this on use the following option:
.option("schema_retention_policy", "collapse_root")or
.option("schema_retention_policy", "keep_original")Let's look at an example. Let's say we have a copybook that looks like this:
01 RECORD.
05 ID PIC S9(4) COMP.
05 COMPANY.
10 SHORT-NAME PIC X(10).
10 COMPANY-ID-NUM PIC 9(5) COMP-3.When "schema_retention_policy" is set to "collapse_root" (default), the root group will be collapsed and the schema will look like this (note the RECORD field is not part of the schema):
root
|-- ID: integer (nullable = true)
|-- COMPANY: struct (nullable = true)
| |-- SHORT_NAME: string (nullable = true)
| |-- COMPANY_ID_NUM: integer (nullable = true)
But when "schema_retention_policy" is set to "keep_original", the schema will look like this (note the RECORD field is part of the schema):
root
|-- RECORD: struct (nullable = true)
| |-- ID: integer (nullable = true)
| |-- COMPANY: struct (nullable = true)
| | |-- SHORT_NAME: string (nullable = true)
| | |-- COMPANY_ID_NUM: integer (nullable = true)
You can experiment with this feature using built-in example in za.co.absa.cobrix.spark.cobol.examples.CobolSparkExample
For data that has record order dependency generation of "File_Id", "Record_Id", and "Record_Byte_Length" fields is supported. The values of the File_Id column will be unique for each file when a directory is specified as the source for data. The values of the Record_Id column will be unique and sequential record identifiers within the file.
Turn this feature on use
.option("generate_record_id", true)
The following fields will be added to the top of the schema:
root
|-- File_Id: integer (nullable = false)
|-- Record_Id: long (nullable = false)
|-- Record_Byte_Length: integer (nullable = false)
You can use this option to generate raw bytes of each record as a binary field:
.option("generate_record_bytes", "true")
The following fields will be added to the top of the schema:
root
|-- Record_Bytes: binary (nullable = false)
Variable-length records depend on headers to have their length calculated, which makes it hard to achieve parallelism while parsing.
Cobrix strives to overcome this drawback by performing a two-stages parsing. The first stage traverses the records retrieving their lengths and offsets into structures called indexes. Then, the indexes are distributed across the cluster, which allows for parallel variable-length records parsing.
However effective, this strategy may also suffer from excessive shuffling, since indexes may be sent to executors far from the actual data.
The latter issue is overcome by extracting the preferred locations for each index directly from HDFS, and then passing those locations to Spark during the creation of the RDD that distributes the indexes.
When processing large collections, the overhead of collecting the locations is offset by the benefits of locality, thus, this feature is enabled by default, but can be disabled by the configuration below:
.option("improve_locality", false)
When dealing with variable-length records, Cobrix strives to maximize locality by identifying the preferred locations in the cluster to parse each record, i.e. the nodes where the record resides.
This feature is implemented by querying HDFS about the locations of the blocks containing each record and instructing Spark to create the partition for that record in one of those locations.
However, sometimes, new nodes can be added to the cluster after the Cobol file is stored, in which case those nodes would be ignored when processing the file since they do not contain any record.
To overcome this issue, Cobrix also strives to re-balance the records among the new nodes at parsing time, as an attempt to maximize the utilization of the cluster. This is done through identifying the busiest nodes and sharing part of their burden with the new ones.
Since this is not an issue present in most cluster configurations, this feature is disabled by default, and can be enabled from the configuration below:
.option("optimize_allocation", true)
If however the option improve_locality is disabled, this option will also be disabled regardless of the value in optimize_allocation.
As you may already know a file in the mainframe world does not mean the same as in the PC world. On PCs we think of a file as a stream of bytes that we can open, read/write and close. On mainframes a file can be a set of records that we can query. Record is a blob of bytes, can have different size. Mainframe's 'filesystem' handles the mapping between logical records and physical location of data.
Details are available at this Wikipedia article (look for MVS filesystem).
So usually a file cannot simply be 'copied' from a mainframe. When files are transferred using tools like XCOM each record is prepended with an additional record header or RDW. This header allows readers of a file in PC to restore the 'set of records' nature of the file.
Mainframe files coming from IMS and copied through specialized tools contain records (the payload) having schema of DBs copybook warped with DB export tool headers wrapped with record headers. Like this:
RECORD_HEADERS ( TOOL_HEADERS ( PAYLOAD ) )
Similar to Internet's TCP protocol IP_HEADERS ( TCP_HEADERS ( PAYLOAD ) ).
TOOL_HEADERS are application dependent. Often it contains the length of the payload. But this length is sometime not very reliable. RECORD_HEADERS contain the record length (including TOOL_HEADERS length) and are proved to be reliable.
For fixed record length files record headers can be ignored since we already know the record length. But for variable record length files and for multisegment files record headers can be considered the most reliable single point of truth about record length.
You can instruct the reader to use 4 byte record headers to extract records from a mainframe file.
.option("record_format", "V")
This is very helpful for multisegment files when segments have different lengths. Since each segment has it's own
copybook it is very convenient to extract segments one by one by combining record_format = V option with segment
filter option.
.option("segment_field", "SEG-ID")
.option("segment_filter", "1122334")
In this example it is expected that the copybook has a field with the name 'SEG-ID'. The data source will read all
segments, but will parse only ones that have SEG-ID = "1122334".
If you want to parse multiple segments, set the option 'segment_filter' to a comma separated list of the segment values. For example:
.option("segment_field", "SEG-ID")
.option("segment_filter", "1122334,1122335")
will only parse the records with SEG-ID = "1122334" OR SEG-ID = "1122335"
Custom record extractors can be used for customizing splitting of input files into a set of records. Cobrix supports
text files, fixed length binary files and binary files with RDWs. If your input file is not in one of the supported
formats you can implement a custom record extractor interface and provide it to spark-cobol as a option:
.option("record_extractor", "com.example.record.header.parser")
A custom record extractor needs to be a class having this precise constructor signature:
class TextRecordExtractor(ctx: RawRecordExtractorParameters) extends Serializable with RawRecordExtractor {
// Your implementation
}A record extractor is essentially iterator of records. Each returned record is an array of bytes parsable by the copybook.
A record extractor is invoked two times. First, it is invoked at the beginning each file to go thought the file and
create a sparse index. The second time it is invoked by parallel processes starting from different records in the file.
The starting record number is provided in constructor. The starting file offset is available from inputStream.
RawRecordContext consists of the following fields that the custom record extractor will get from Cobrix in runtime:
startingRecordNumber- A record number the input stream is pointing to.inputStream- The input stream of bytes of the input file.copybook- The parsed copybook of the input stream.additionalInfo- An arbitrary info that can be passed as an option (see below).
If your record extractor needs additional information in order to extract records properly, you can provide an arbitrary additional info to the record extracted at runtime by specifying this option:
Take a look at CustomRecordExtractorMock inside spark-cobol project to see how a custom record extractor can be built.
.option("re_additional_info", "some info")
Custom record header parsers are deprecated. Use custom record extractors instead. They are more flexible and easier to use.
If your variable length file does not have RDW headers, but has fields that can be used for determining record lengths
you can provide a custom record header parser that takes starting bytes of each record and returns record lengths.
In order to do that you need to create a class inheriting RecordHeaderParser and Serializable traits and provide a
fully qualified class name to the following option:
.option("record_header_parser", "com.example.record.header.parser")
Cobrix provides helper methods to convert RDD[String] or RDD[Array[Byte]] to DataFrame using a copybook.
This can be used if you want to use a custom logic to split the input file into records as either ASCII strings
or arrays of bytes, and then parse each record using a copybook.
An example of RDD[Array[Byte]]:
import za.co.absa.cobrix.spark.cobol.Cobrix
val rdd = ???
val df = Cobrix.fromRdd
.copybookContents(copybook)
.option("encoding", "ebcdic") // any supported option
.load(rdd)An example of ASCII Strings RDD[String]:
import za.co.absa.cobrix.spark.cobol.Cobrix
val rdd = ???
val df = Cobrix.fromRdd
.copybookContents(copybook)
.option("variable_size_occurs", "true") // any supported option
.loadText(rdd)When converting from an RDD some of the options like record_format or generate_record_id cannot be used since the
data is assumed to be already split by records and the information about file names and relative order of records is not available.
The following code pages are supported:
common- (default) EBCDIC common characterscommon_extended- EBCDIC common characters with special characters extensioncp037- IBM EBCDIC US-Canadacp037_extended- IBM EBCDIC US-Canada with special characters extensioncp300- IBM EBCDIC Japanese Extended (2 byte code page)cp838- IBM EBCDIC Thailandcp870- IBM EBCDIC Multilingual Latin-2cp875- IBM EBCDIC Greekcp1025- IBM EBCDIC Multilingual Cyrilliccp1047- IBM EBCDIC Latin-1/Open Systemcp1364- (experimental support) IBM EBCDIC Korean (2 byte code page)cp1388- (experimental support) IBM EBCDIC Simplified Chinese (2 byte code page)
By default, Cobrix uses common EBCDIC code page which contains only basic latin characters, numbers, and punctuation.
You can specify the code page to use for all string fields by setting the ebcdic_code_page option to one of the
following values:
.option("ebcdic_code_page", "cp037")
For multi-codepage files, you can specify the code page to use for each field by setting the field_code_page:<code page> option
.option("ebcdic_code_page", "cp037")
.option("field_code_page:cp1256" -> "FIELD1")
.option("field_code_page:us-ascii" -> "FIELD-2, FIELD_3")
Cobrix is primarily designed to read binary files, but you can directly use some internal functions to read ASCII text files. In ASCII text files, records are separated with newlines.
Working example 1:
// The recommended way
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("ascii_charset", "ISO-8859-1") // You can choose a charset, UTF-8 is used by default
.option("record_format", "D")
.load(tmpFileName)Working example 2 - Using RDDs and helper methods:
// This is the way if you have data converted to an RDD[String] already.
// You have full control on reading the input data records and converting them to `java.lang.String`.
val df = Cobrix.fromRdd
.copybookContents(copybook)
.option("variable_size_occurs", "true") // any supported option
.loadText(rdd)Working example 3 - Using RDDs and record parsers directly:
// This is the most verbose way - creating dataframes from RDDs. But it gives full control on how text files are
// processed before parsing actual records
val spark = SparkSession
.builder()
.appName("Spark-Cobol ASCII text file")
.master("local[*]")
.getOrCreate()
val copybook =
""" 01 COMPANY-DETAILS.
| 05 SEGMENT-ID PIC 9(1).
| 05 STATIC-DETAILS.
| 10 NAME PIC X(2).
|
| 05 CONTACTS REDEFINES STATIC-DETAILS.
| 10 PERSON PIC X(3).
""".stripMargin
val parsedCopybook = CopybookParser.parse(copybook, dataEnncoding = ASCII, stringTrimmingPolicy = StringTrimmingPolicy.TrimNone)
val cobolSchema = new CobolSchema(parsedCopybook, SchemaRetentionPolicy.CollapseRoot, "", false)
val sparkSchema = cobolSchema.getSparkSchema
val rddText = spark.sparkContext.textFile("src/main/resources/mini.txt")
val recordHandler = new RowHandler()
val rddRow = rddText
.filter(str => str.length > 0)
.map(str => {
val record = RecordExtractors.extractRecord[GenericRow](parsedCopybook.ast,
str.getBytes(),
0,
SchemaRetentionPolicy.CollapseRoot, handler = recordHandler)
Row.fromSeq(record)
})
val dfOut = spark.createDataFrame(rddRow, sparkSchema)
dfOut.printSchema()
dfOut.show()Corresponding data sample in mini.txt:
1BB
2CCC
Output:
root
|-- SEGMENT_ID: integer (nullable = true)
|-- STATIC_DETAILS: struct (nullable = true)
| |-- NAME: string (nullable = true)
|-- CONTACTS: struct (nullable = true)
| |-- PERSON: string (nullable = true)
...
+----------+--------------+--------+
|SEGMENT_ID|STATIC_DETAILS|CONTACTS|
+----------+--------------+--------+
| 1| [BB]| [null]|
| 2| [CC]| [CCC]|
+----------+--------------+--------+
There, Cobrix loaded all redefines for every record. Each record contains data from all of the segments. But only one redefine is valid for every segment. Filtering is described in the following section.
When reading a multisegment file you can use Spark to clean up redefines that do not match segment ids. Cobrix will parse every redefined field for each segment. To increase performance you can specify which redefine corresponds to which segment id. This way Cobrix will parse only relevant segment redefined fields and leave the rest of the redefined fields null.
.option("redefine-segment-id-map:0", "REDEFINED_FIELD1 => SegmentId1,SegmentId2,...")
.option("redefine-segment-id-map:1", "REDEFINED_FIELD2 => SegmentId10,SegmentId11,...")
For the above example the load options will lok like this (last 2 options):
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("record_format", "V")
.option("segment_field", "SEGMENT_ID")
.option("segment_id_level0", "C")
.option("segment_id_level1", "P")
.option("redefine_segment_id_map:0", "STATIC-DETAILS => C")
.option("redefine_segment_id_map:1", "CONTACTS => P")
.load("examples/multisegment_data/COMP.DETAILS.SEP30.DATA.dat")The filtered data will look like this:
df.show(10)
+----------+----------+--------------------+--------------------+
|SEGMENT_ID|COMPANY_ID| STATIC_DETAILS| CONTACTS|
+----------+----------+--------------------+--------------------+
| C|9377942526|[Joan Q & Z,10 Sa...| |
| P|9377942526| |[+(277) 944 44 55...|
| C|3483483977|[Robotrd Inc.,2 P...| |
| P|3483483977| |[+(174) 970 97 54...|
| P|3483483977| |[+(848) 832 61 68...|
| P|3483483977| |[+(455) 184 13 39...|
| C|7540764401|[Eqartion Inc.,87...| |
| C|4413124035|[Xingzhoug,74 Qin...| |
| C|9546291887|[ZjkLPj,5574, Tok...| |
| P|9546291887| |[+(300) 252 33 17...|
+----------+----------+--------------------+--------------------+
In the above example invalid fields became null and the parsing is done faster because Cobrix does not need to process
every redefine for each record.
A FILLER is an anonymous field that is usually used for reserving space for new fields in a fixed record length data. Or it is used to remove a field from a copybook without affecting compatibility.
05 COMPANY.
10 NAME PIC X(15).
10 FILLER PIC X(5).
10 ADDRESS PIC X(25).
10 FILLER PIC X(125).Such fields are dropped when imported into a Spark data frame by Cobrix. Some copybooks, however, have FILLER groups that contain non-filler fields. For example,
05 FILLER.
10 NAME PIC X(15).
10 ADDRESS PIC X(25).
05 FILLER.
10 AMOUNT PIC 9(10)V96.
10 COMMENT PIC X(40).By default Cobrix will retain such fields, but will rename each such filler to a unique name so each each individual struct
can be specified unambiguously. For example, in this case the filler groups will be renamed to FILLER_1 and FILLER_2.
You can change this behaviour if you would like to drop such filler groups by providing this option:
.option("drop_group_fillers", "true")
In order to retain value FILLERs (e.g. non-group FILLERs) as well, use this option:
.option("drop_value_fillers", "false")
Let's imagine we have a multisegment file with 2 segments having parent-child relationships. Each segment has a different record type. The root record/segment contains company info, an address and a taxpayer number. The child segment contains a contact person for a company. Each company can have zero or more contact persons. So each root record can be followed by zero or more child records.
To load such data in Spark the first thing you need to do is to create a copybook that contains all segment specific fields in redefined groups. Here is the copybook for our example:
01 COMPANY-DETAILS.
05 SEGMENT-ID PIC X(5).
05 COMPANY-ID PIC X(10).
05 STATIC-DETAILS.
10 COMPANY-NAME PIC X(15).
10 ADDRESS PIC X(25).
10 TAXPAYER.
15 TAXPAYER-TYPE PIC X(1).
15 TAXPAYER-STR PIC X(8).
15 TAXPAYER-NUM REDEFINES TAXPAYER-STR
PIC 9(8) COMP.
05 CONTACTS REDEFINES STATIC-DETAILS.
10 PHONE-NUMBER PIC X(17).
10 CONTACT-PERSON PIC X(28).The 'SEGMENT-ID' and 'COMPANY-ID' fields are present in all of the segments. The 'STATIC-DETAILS' group is present only in the root record. The 'CONTACTS' group is present only in child record. Notice that 'CONTACTS' redefine 'STATIC-DETAILS'.
Because the records have different lengths use record_format = V or record_format = VB depending of the record format.
If you load this file as is you will get the schema and the data similar to this.
val df = spark
.read
.format("cobol")
.option("copybook", "/path/to/thecopybook")
.option("record_format", "V")
.load("examples/multisegment_data")df.printSchema()
root
|-- SEGMENT_ID: string (nullable = true)
|-- COMPANY_ID: string (nullable = true)
|-- STATIC_DETAILS: struct (nullable = true)
| |-- COMPANY_NAME: string (nullable = true)
| |-- ADDRESS: string (nullable = true)
| |-- TAXPAYER: struct (nullable = true)
| | |-- TAXPAYER_TYPE: string (nullable = true)
| | |-- TAXPAYER_STR: string (nullable = true)
| | |-- TAXPAYER_NUM: integer (nullable = true)
|-- CONTACTS: struct (nullable = true)
| |-- PHONE_NUMBER: string (nullable = true)
| |-- CONTACT_PERSON: string (nullable = true)
df.show(10)
+----------+----------+--------------------+--------------------+
|SEGMENT_ID|COMPANY_ID| STATIC_DETAILS| CONTACTS|
+----------+----------+--------------------+--------------------+
| C|9377942526|[Joan Q & Z,10 Sa...|[Joan Q & Z 1...|
| P|9377942526|[+(277) 944 44 5,...|[+(277) 944 44 55...|
| C|3483483977|[Robotrd Inc.,2 P...|[Robotrd Inc. 2...|
| P|3483483977|[+(174) 970 97 5,...|[+(174) 970 97 54...|
| P|3483483977|[+(848) 832 61 6,...|[+(848) 832 61 68...|
| P|3483483977|[+(455) 184 13 3,...|[+(455) 184 13 39...|
| C|7540764401|[Eqartion Inc.,87...|[Eqartion Inc. 8...|
| C|4413124035|[Xingzhoug,74 Qin...|[Xingzhoug 7...|
| C|9546291887|[ZjkLPj,5574, Tok...|[ZjkLPj 5...|
| P|9546291887|[+(300) 252 33 1,...|[+(300) 252 33 17...|
+----------+----------+--------------------+--------------------+
As you can see Cobrix loaded all redefines for every record. Each record contains data from all of the segments. But only one redefine is valid for every segment. So we need to split the data set into 2 datasets or tables. The distinguisher is the 'SEGMENT_ID' field. All company details will go into one data sets (segment id = 'C' [company]) while contacts will go in the second data set (segment id = 'P' [person]). While doing the split we can also collapse the groups so the table won't contain nested structures. This can be helpful to simplify the analysis of the data.
While doing it you might notice that the taxpayer number field is actually a redefine. Depending on the 'TAXPAYER_TYPE' either 'TAXPAYER_NUM' or 'TAXPAYER_STR' is used. We can resolve this in our Spark app as well.
Starting from spark-cobol version 1.1.0 hierarchical structure of multisegment records can be restored automatically. In order to do this you
need to provide:
- A segment ID field that will be used to distinguish segment types.
- A segmentId to redefine fields mapping that will be used to map each segment to a redefine field.
- A parent-child relationship between segments identified by segment redefine fields.
When all of the above is specified Cobrix can reconstruct hierarchical nature of records by making child segments nested arrays of parent segments. Arbitrary levels of hierarchy and arbitrary number of segments is supported.
val df = spark
.read
.format("cobol")
.option("copybook", "/path/to/thecopybook")
.option("record_format", "V")
// Specifies a field containing a segment id
.option("segment_field", "SEGMENT_ID")
// Specifies a mapping between segment ids and segment redefine fields
.option("redefine_segment_id_map:1", "STATIC-DETAILS => C")
.option("redefine-segment-id-map:2", "CONTACTS => P")
// Specifies a parent-child relationship
.option("segment-children:1", "STATIC-DETAILS => CONTACTS")
.load("examples/multisegment_data")The output schema will be
scala> df.printSchema()
root
|-- SEGMENT_ID: string (nullable = true)
|-- COMPANY_ID: string (nullable = true)
|-- STATIC_DETAILS: struct (nullable = true)
| |-- COMPANY_NAME: string (nullable = true)
| |-- ADDRESS: string (nullable = true)
| |-- TAXPAYER: struct (nullable = true)
| | |-- TAXPAYER_TYPE: string (nullable = true)
| | |-- TAXPAYER_STR: string (nullable = true)
| | |-- TAXPAYER_NUM: integer (nullable = true)
| |-- CONTACTS: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- PHONE_NUMBER: string (nullable = true)
| | | |-- CONTACT_PERSON: string (nullable = true)
Notice that contacts now is an array of structs. That is a company static details can contain zero or mor contacts. A possible hierarchical record output is
scala> import za.co.absa.cobrix.spark.cobol.utils.SparkUtils
scala> println(SparkUtils.prettyJSON(df.toJSON.take(1).mkString("[", ", ", "]")))
{
"SEGMENT_ID" : "C",
"COMPANY_ID" : "9377942526",
"STATIC_DETAILS" : {
"COMPANY_NAME" : "Joan Q & Z",
"ADDRESS" : "10 Sandton, Johannesburg",
"TAXPAYER" : {
"TAXPAYER_TYPE" : "A",
"TAXPAYER_STR" : "92714306",
"TAXPAYER_NUM" : 959592241
},
"CONTACTS" : [ {
"PHONE_NUMBER" : "+(174) 970 97 54",
"CONTACT_PERSON" : "Tyesha Debow"
}, {
"PHONE_NUMBER" : "+(848) 832 61 68",
"CONTACT_PERSON" : "Mindy Celestin"
}, {
"PHONE_NUMBER" : "+(455) 184 13 39",
"CONTACT_PERSON" : "Mabelle Winburn"
} ]
}
}
An advanced hierarchical example with multiple levels of nesting and multiple segments on each level
is available as a unit test za/co/absa/cobrix/spark/cobol/source/integration/Test17HierarchicalSpec.scala.
Alternatively, hierarchical record structure can be reconstructed manually by extracting each segment and joining segments together. This a is more complicated process, but it provides more control.
import spark.implicits._
val dfCompanies = df
// Filtering the first segment by segment id
.filter($"SEGMENT_ID"==="C")
// Selecting fields that are only available in the first segment
.select($"COMPANY_ID", $"STATIC_DETAILS.COMPANY_NAME", $"STATIC_DETAILS.ADDRESS",
// Resolving the taxpayer redefine
when($"STATIC_DETAILS.TAXPAYER.TAXPAYER_TYPE" === "A", $"STATIC_DETAILS.TAXPAYER.TAXPAYER_STR")
.otherwise($"STATIC_DETAILS.TAXPAYER.TAXPAYER_NUM").cast(StringType).as("TAXPAYER"))The resulting table looks like this:
dfCompanies.show(10, truncate = false)
+----------+-------------+-------------------------+--------+
|COMPANY_ID|COMPANY_NAME |ADDRESS |TAXPAYER|
+----------+-------------+-------------------------+--------+
|9377942526|Joan Q & Z |10 Sandton, Johannesburg |92714306|
|3483483977|Robotrd Inc. |2 Park ave., Johannesburg|31195396|
|7540764401|Eqartion Inc.|871A Forest ave., Toronto|87432264|
|4413124035|Xingzhoug |74 Qing ave., Beijing |50803302|
|9546291887|ZjkLPj |5574, Tokyo |73538919|
|9168453994|Test Bank |1 Garden str., London |82573513|
|4225784815|ZjkLPj |5574, Tokyo |96136195|
|8463159728|Xingzhoug |74 Qing ave., Beijing |17785468|
|8180356010|Eqartion Inc.|871A Forest ave., Toronto|79054306|
|7107728116|Xingzhoug |74 Qing ave., Beijing |70899995|
+----------+-------------+-------------------------+--------+
This looks like a valid and clean table containing the list of companies. Now let's do the same for the second segment.
val dfContacts = df
// Filtering the second segment by segment id
.filter($"SEGMENT_ID"==="P")
// Selecting the fields only valid for the second segment
.select($"COMPANY_ID", $"CONTACTS.CONTACT_PERSON", $"CONTACTS.PHONE_NUMBER")The resulting data loons like this:
dfContacts.show(10, truncate = false)
+----------+--------------------+----------------+
|COMPANY_ID|CONTACT_PERSON |PHONE_NUMBER |
+----------+--------------------+----------------+
|9377942526|Janiece Newcombe |+(277) 944 44 55|
|3483483977|Tyesha Debow |+(174) 970 97 54|
|3483483977|Mindy Celestin |+(848) 832 61 68|
|3483483977|Mabelle Winburn |+(455) 184 13 39|
|9546291887|Carrie Celestin |+(300) 252 33 17|
|9546291887|Edyth Deveau |+(907) 101 70 64|
|9546291887|Jene Norgard |+(694) 918 17 44|
|9168453994|Timika Bourke |+(768) 691 44 85|
|9168453994|Lynell Riojas |+(695) 918 33 16|
|4225784815|Jene Mackinnon |+(540) 937 33 71|
+----------+--------------------+----------------+
This looks good as well. The table contains the list of contact persons for companies. This data set contains the 'COMPANY_ID' field which we can use later to join the tables. But often there are no such fields in data imported from hierarchical databases. If that is the case Cobrix can help you craft such fields automatically. Use 'segment_field' to specify a field that contain the segment id. Use 'segment_id_level0' to ask Cobrix to generate ids for the particular segments. We can use 'segment_id_level1' to generate child ids as well. If children records can contain children of their own we can use 'segment_id_level2' etc.
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("record_format", "V")
.option("segment_field", "SEGMENT_ID")
.option("segment_id_level0", "C")
.option("segment_id_level1", "P")
.load("examples/multisegment_data/COMP.DETAILS.SEP30.DATA.dat")Sometimes, the leaf level has many segments. In this case, you can use _ as the list of segment ids to specify
'the rest of segment ids', like this:
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("record_format", "V")
.option("segment_field", "SEGMENT_ID")
.option("segment_id_level0", "C")
.option("segment_id_level1", "_")
.load("examples/multisegment_data/COMP.DETAILS.SEP30.DATA.dat")The result of both above code snippets is the same.
The resulting table will look like this:
df.show(10)
+------------------+-----------------------+----------+----------+--------------------+--------------------+
| Seg_Id0| Seg_Id1|SEGMENT_ID|COMPANY_ID| STATIC_DETAILS| CONTACTS|
+------------------+-----------------------+----------+----------+--------------------+--------------------+
|20181219130609_0_0| null| C|9377942526|[Joan Q & Z,10 Sa...|[Joan Q & Z 1...|
|20181219130609_0_0|20181219130723_0_0_L1_1| P|9377942526|[+(277) 944 44 5,...|[+(277) 944 44 55...|
|20181219130609_0_2| null| C|3483483977|[Robotrd Inc.,2 P...|[Robotrd Inc. 2...|
|20181219130609_0_2|20181219130723_0_2_L1_1| P|3483483977|[+(174) 970 97 5,...|[+(174) 970 97 54...|
|20181219130609_0_2|20181219130723_0_2_L1_2| P|3483483977|[+(848) 832 61 6,...|[+(848) 832 61 68...|
|20181219130609_0_2|20181219130723_0_2_L1_3| P|3483483977|[+(455) 184 13 3,...|[+(455) 184 13 39...|
|20181219130609_0_6| null| C|7540764401|[Eqartion Inc.,87...|[Eqartion Inc. 8...|
|20181219130609_0_7| null| C|4413124035|[Xingzhoug,74 Qin...|[Xingzhoug 7...|
|20181219130609_0_8| null| C|9546291887|[ZjkLPj,5574, Tok...|[ZjkLPj 5...|
|20181219130609_0_8|20181219130723_0_8_L1_1| P|9546291887|[+(300) 252 33 1,...|[+(300) 252 33 17...|
+------------------+-----------------------+----------+----------+--------------------+--------------------+
The data now contain 2 additional fields: 'Seg_Id0' and 'Seg_Id1'. The 'Seg_Id0' is an autogenerated id for each root record. It is also unique for a root record. After splitting the segments you can use Seg_Id0 to join both tables. The 'Seg_Id1' field contains a unique child id. It is equal to 'null' for all root records but uniquely identifies child records.
You can now split these 2 segments and join them by Seg_Id0. The full example is available at
spark-cobol/src/main/scala/za/co/absa/cobrix/spark/cobol/examples/CobolSparkExample2.scala
To run it from an IDE you'll need to change Scala and Spark dependencies from 'provided' to 'compile' so the
jar file would contain all the dependencies. This is because Cobrix is a library to be used in Spark job projects.
Spark jobs uber jars should not contain Scala and Spark dependencies since Hadoop clusters have their Scala and Spark
dependencies provided by the infrastructure. Including Spark and Scala dependencies in an uber jar can produce
binary incompatibilities when these jars are used in spark-submit and spark-shell.
Here is our example tables to join:
dfCompanies.show(10, truncate = false)
+--------------------+----------+-------------+-------------------------+--------+
|Seg_Id0 |COMPANY_ID|COMPANY_NAME |ADDRESS |TAXPAYER|
+--------------------+----------+-------------+-------------------------+--------+
|20181219130723_0_0 |9377942526|Joan Q & Z |10 Sandton, Johannesburg |92714306|
|20181219130723_0_2 |3483483977|Robotrd Inc. |2 Park ave., Johannesburg|31195396|
|20181219130723_0_6 |7540764401|Eqartion Inc.|871A Forest ave., Toronto|87432264|
|20181219130723_0_7 |4413124035|Xingzhoug |74 Qing ave., Beijing |50803302|
|20181219130723_0_8 |9546291887|ZjkLPj |5574, Tokyo |73538919|
|20181219130723_0_12 |9168453994|Test Bank |1 Garden str., London |82573513|
|20181219130723_0_15 |4225784815|ZjkLPj |5574, Tokyo |96136195|
|20181219130723_0_20 |8463159728|Xingzhoug |74 Qing ave., Beijing |17785468|
|20181219130723_0_24 |8180356010|Eqartion Inc.|871A Forest ave., Toronto|79054306|
|20181219130723_0_27 |7107728116|Xingzhoug |74 Qing ave., Beijing |70899995|
+--------------------+----------+-------------+-------------------------+--------+
dfContacts.show(13, truncate = false)
+-------------------+----------+-------------------+----------------+
|Seg_Id0 |COMPANY_ID|CONTACT_PERSON |PHONE_NUMBER |
+-------------------+----------+-------------------+----------------+
|20181219130723_0_0 |9377942526|Janiece Newcombe |+(277) 944 44 55|
|20181219130723_0_2 |3483483977|Tyesha Debow |+(174) 970 97 54|
|20181219130723_0_2 |3483483977|Mindy Celestin |+(848) 832 61 68|
|20181219130723_0_2 |3483483977|Mabelle Winburn |+(455) 184 13 39|
|20181219130723_0_8 |9546291887|Carrie Celestin |+(300) 252 33 17|
|20181219130723_0_8 |9546291887|Edyth Deveau |+(907) 101 70 64|
|20181219130723_0_8 |9546291887|Jene Norgard |+(694) 918 17 44|
|20181219130723_0_12|9168453994|Timika Bourke |+(768) 691 44 85|
|20181219130723_0_12|9168453994|Lynell Riojas |+(695) 918 33 16|
|20181219130723_0_15|4225784815|Jene Mackinnon |+(540) 937 33 71|
|20181219130723_0_15|4225784815|Timika Concannon |+(122) 216 11 25|
|20181219130723_0_15|4225784815|Jene Godfrey |+(285) 643 50 47|
|20181219130723_0_15|4225784815|Gabriele Winburn |+(489) 644 53 67|
+-------------------+----------+-------------------+----------------+
Let's now join these tables.
The join statement in Spark:
val dfJoined = dfCompanies.join(dfContacts, "Seg_Id0")The joined data looks like this:
dfJoined.show(13, truncate = false)
+--------------------+----------+-------------+-------------------------+--------+----------+--------------------+----------------+
|Seg_Id0 |COMPANY_ID|COMPANY_NAME |ADDRESS |TAXPAYER|COMPANY_ID|CONTACT_PERSON |PHONE_NUMBER |
+--------------------+----------+-------------+-------------------------+--------+----------+--------------------+----------------+
|20181219130723_0_0 |9377942526|Joan Q & Z |10 Sandton, Johannesburg |92714306|9377942526|Janiece Newcombe |+(277) 944 44 55|
|20181219131239_0_2 |3483483977|Robotrd Inc. |2 Park ave., Johannesburg|31195396|3483483977|Mindy Celestin |+(848) 832 61 68|
|20181219131239_0_2 |3483483977|Robotrd Inc. |2 Park ave., Johannesburg|31195396|3483483977|Tyesha Debow |+(174) 970 97 54|
|20181219131239_0_2 |3483483977|Robotrd Inc. |2 Park ave., Johannesburg|31195396|3483483977|Mabelle Winburn |+(455) 184 13 39|
|20181219131344_0_8 |9546291887|ZjkLPj |5574, Tokyo |73538919|9546291887|Jene Norgard |+(694) 918 17 44|
|20181219131344_0_8 |9546291887|ZjkLPj |5574, Tokyo |73538919|9546291887|Edyth Deveau |+(907) 101 70 64|
|20181219131344_0_8 |9546291887|ZjkLPj |5574, Tokyo |73538919|9546291887|Carrie Celestin |+(300) 252 33 17|
|20181219131344_0_12 |9168453994|Test Bank |1 Garden str., London |82573513|9168453994|Timika Bourke |+(768) 691 44 85|
|20181219131344_0_12 |9168453994|Test Bank |1 Garden str., London |82573513|9168453994|Lynell Riojas |+(695) 918 33 16|
|20181219131344_0_15 |4225784815|ZjkLPj |5574, Tokyo |96136195|4225784815|Jene Mackinnon |+(540) 937 33 71|
|20181219131344_0_15 |4225784815|ZjkLPj |5574, Tokyo |96136195|4225784815|Timika Concannon |+(122) 216 11 25|
|20181219131344_0_15 |4225784815|ZjkLPj |5574, Tokyo |96136195|4225784815|Jene Godfrey |+(285) 643 50 47|
|20181219131344_0_15 |4225784815|ZjkLPj |5574, Tokyo |96136195|4225784815|Gabriele Winburn |+(489) 644 53 67|
+--------------------+----------+-------------+-------------------------+--------+----------+--------------------+----------------+
Again, the full example is available at
spark-cobol/src/main/scala/za/co/absa/cobrix/spark/cobol/examples/CobolSparkExample2.scala
Some encoding formats are not expressible by the standard copybook spec. Cobrix has extensions to help you decode fields encoded in this way.
Loading multiple paths in the standard way is not supported.
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.load("/path1", "/paths2")But there is a Cobrix extension that allows you to load multiple paths:
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("data_paths", "/path1,/paths2")
.load()Cobrix expects all binary numbers to be big-endian. If you have a binary number in the little-endian format, use
COMP-9 (Cobrix extension) instead of COMP or COMP-5 for the affected fields.
For example, 0x01 0x02 is 1 + 2*256 = 513 in big-endian (COMP) and 1*256 + 2 = 258 (COMP-9) in little-endian.
10 NUM PIC S9(8) COMP. ** Big-endian
10 NUM PIC S9(8) COMP-9. ** Little-endian
Unsigned backed numbers are encoded as BCD (COMP-3) without the sign nibble. For example, bytes 0x12 0x34 encode
the number 1234. As of 2.6.2 Cobrix supports decoding such numbers using an extension. Use COMP-3U for unsigned
packed numbers.
The 'COMP-3U' usage
10 NUM PIC X(4) COMP-3U.
Note that when using X 4 refers to the number of bytes the field occupies. Here, the number of digits is 4*2 = 8.
10 NUM PIC 9(8) COMP-3U.
When using 9 8 refers to the number of digits the number has. Here, the size of the field in bytes is 8/2 = 4.
10 NUM PIC 9(6)V99 COMP-3U.
You can have decimals when using COMP-3 as well.
Flattening could be helpful when migrating data from mainframe data with fields that have OCCURs (arrays) to a relational databases that do not support nested arrays.
Cobrix has a method that can flatten the schema automatically given a DataFrame produced by spark-cobol.
Spark Scala example:
val dfFlat = SparkUtils.flattenSchema(df, useShortFieldNames = false)PySpark example
from pyspark.sql import SparkSession, DataFrame, SQLContext
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, ArrayType
from py4j.java_gateway import java_import
schema = StructType([
StructField("id", IntegerType(), True),
StructField("name", StringType(), True),
StructField("subjects", ArrayType(StringType()), True)
])
# Sample data
data = [
(1, "Alice", ["Math", "Science"]),
(2, "Bob", ["History", "Geography"]),
(3, "Charlie", ["English", "Math", "Physics"])
]
# Create a test DataFrame
df = spark.createDataFrame(data, schema)
# Show the Dataframe before flattening
df.show()
# Flatten the schema using Cobrix Scala 'SparkUtils.flattenSchema' method
sc = spark.sparkContext
java_import(sc._gateway.jvm, "za.co.absa.cobrix.spark.cobol.utils.SparkUtils")
dfFlatJvm = spark._jvm.SparkUtils.flattenSchema(df._jdf, False)
dfFlat = DataFrame(dfFlatJvm, SQLContext(sc))
# Show the Dataframe after flattening
dfFlat.show(truncate=False)
dfFlat.printSchema()The output looks like this:
# Before flattening
+---+-------+------------------------+
|id |name |subjects |
+---+-------+------------------------+
|1 |Alice |[Math, Science] |
|2 |Bob |[History, Geography] |
|3 |Charlie|[English, Math, Physics]|
+---+-------+------------------------+
# After flattening
+---+-------+----------+----------+----------+
|id |name |subjects_0|subjects_1|subjects_2|
+---+-------+----------+----------+----------+
|1 |Alice |Math |Science |null |
|2 |Bob |History |Geography |null |
|3 |Charlie|English |Math |Physics |
+---+-------+----------+----------+----------+
| Option (usage example) | Description |
|---|---|
| .option("data_paths", "/path1,/path2") | Allows loading data from multiple unrelated paths on the same filesystem. |
| .option("file_start_offset", "0") | Specifies the number of bytes to skip at the beginning of each file. |
| .option("file_end_offset", "0") | Specifies the number of bytes to skip at the end of each file. |
| .option("record_start_offset", "0") | Specifies the number of bytes to skip at the beginning of each record before applying copybook fields to data. |
| .option("record_end_offset", "0") | Specifies the number of bytes to skip at the end of each record after applying copybook fields to data. |
| Option (usage example) | Description |
|---|---|
| .option("truncate_comments", "true") | Historically, COBOL parser ignores the first 6 characters and all characters after 72. When this option is false, no truncation is performed. |
| .option("comments_lbound", 6) | By default each line starts with a 6 character comment. The exact number of characters can be tuned using this option. |
| .option("comments_ubound", 72) | By default all characters after 72th one of each line is ignored by the COBOL parser. The exact number of characters can be tuned using this option. |
| Option (usage example) | Description |
|---|---|
| .option("string_trimming_policy", "both") | Specifies if and how string fields should be trimmed. Available options: both (default), none, left, right, keep_all. keep_all - keeps control characters when decoding ASCII text files |
| .option("ebcdic_code_page", "common") | Specifies a code page for EBCDIC encoding. Currently supported values: common (default), common_extended, cp037, cp037_extended, and others (see "Currently supported EBCDIC code pages" section. |
| .option("ebcdic_code_page_class", "full.class.specifier") | Specifies a user provided class for a custom code page to UNICODE conversion. |
| .option("field_code_page:cp825", "field1, field2") | Specifies the code page for selected fields. You can add mo than 1 such option for multiple code page overrides. |
| .option("is_utf16_big_endian", "true") | Specifies if UTF-16 encoded strings (National / PIC N format) are big-endian (default). |
| .option("floating_point_format", "IBM") | Specifies a floating-point format. Available options: IBM (default), IEEE754, IBM_little_endian, IEEE754_little_endian. |
| .option("variable_size_occurs", "false") | If false (default) fields that have OCCURS 0 TO 100 TIMES DEPENDING ON clauses always have the same size corresponding to the maximum array size (e.g. 100 in this example). If set to true the size of the field will shrink for each field that has less actual elements. |
| .option("occurs_mapping", "{"FIELD": {"X": 1}}") | If specified, as a JSON string, allows for String DEPENDING ON fields with a corresponding mapping. |
| .option("strict_sign_overpunching", "true") | If true (default), sign overpunching will only be allowed for signed numbers. If false, overpunched positive sign will be allowed for unsigned numbers, but negative sign will result in null. |
| .option("improved_null_detection", "true") | If true(default), values that contain only 0x0 ror DISPLAY strings and numbers will be considered nulls instead of empty strings. |
| .option("strict_integral_precision", "true") | If true, Cobrix will not generate short/integer/long Spark data types, and always use decimal(n) with the exact precision that matches the copybook. |
| .option("binary_as_hex", "false") | By default fields that have PIC X and USAGE COMP are converted to binary Spark data type. If this option is set to true, such fields will be strings in HEX encoding. |
| Option (usage example) | Description |
|---|---|
| .option("schema_retention_policy", "collapse_root") | When collapse_root (default) the root level record will be removed from the Spark schema. When keep_original, the root level GROUP will be present in the Spark schema |
| .option("drop_group_fillers", "false") | If true, all GROUP FILLERs will be dropped from the output schema. If false (default), such fields will be retained. |
| .option("drop_value_fillers", "false") | If true (default), all non-GROUP FILLERs will be dropped from the output schema. If false, such fields will be retained. |
| .option("filler_naming_policy", "sequence_numbers") | Filler renaming strategy so that column names are not duplicated. Either sequence_numbers (default) or previous_field_name can be used. |
| .option("non_terminals", "GROUP1,GROUP2") | Specifies groups to also be added to the schema as string fields. When this option is specified, the reader will add one extra data field after each matching group containing the string data for the group. |
| .option("generate_record_id", false) | Generate autoincremental 'File_Id', 'Record_Id' and 'Record_Byte_Length' fields. This is used for processing record order dependent data. |
| .option("generate_record_bytes", false) | Generate 'Record_Bytes', the binary field that contains raw contents of the original unparsed records. |
| .option("with_input_file_name_col", "file_name") | Generates a column containing input file name for each record (Similar to Spark SQL input_file_name() function). The column name is specified by the value of the option. This option only works for variable record length files. For fixed record length and ASCII files use input_file_name(). |
| .option("metadata", "basic") | Specifies wat kind of metadata to include in the Spark schema: false, basic(default), or extended (PIC, usage, etc). |
| .option("debug", "hex") | If specified, each primitive field will be accompanied by a debug field containing raw bytes from the source file. Possible values: none (default), hex, binary, string (ASCII only). The legacy value true is supported and will generate debug fields in HEX. |
| Option (usage example) | Description |
|---|---|
| .option("record_format", "F") | Record format from the spec. One of F (fixed length, default), FB (fixed block), V(variable length RDW),VB(variable block BDW+RDW),D` (ASCII text). |
| .option("record_length", "100") | Overrides the length of the record (in bypes). Normally, the size is derived from the copybook. But explicitly specifying record size can be helpful for debugging fixed-record length files. |
| .option("block_length", "500") | Specifies the block length for FB records. It should be a multiple of 'record_length'. Cannot be used together with records_per_block |
| .option("records_per_block", "5") | Specifies the number of records ber block for FB records. Cannot be used together with block_length |
| Option (usage example) | Description |
|---|---|
| .option("record_format", "V") | Record format from the spec. One of F (fixed length, default), FB (fixed block), V(variable length RDW),VB(variable block BDW+RDW),D` (ASCII text). |
| .option("is_record_sequence", "true") | [deprecated] If 'true' the parser will look for 4 byte RDW headers to read variable record length files. Use .option("record_format", "V") instead. |
| .option("is_rdw_big_endian", "true") | Specifies if RDW headers are big endian. They are considered little-endian by default. |
| .option("is_rdw_part_of_record_length", false) | Specifies if RDW headers count themselves as part of record length. By default RDW headers count only payload record in record length, not RDW headers themselves. This is equivalent to .option("rdw_adjustment", -4). For BDW use .option("bdw_adjustment", -4) |
| .option("rdw_adjustment", 0) | If there is a mismatch between RDW and record length this option can be used to adjust the difference. |
| .option("bdw_adjustment", 0) | If there is a mismatch between BDW and record length this option can be used to adjust the difference. |
| .option("re_additional_info", "") | Passes a string as an additional info parameter passed to a custom record extractor to its constructor. |
| .option("record_length_field", "RECORD-LEN") | Specifies a record length field or expression to use instead of RDW. Use rdw_adjustment option if the record length field differs from the actual length by a fixed amount of bytes. The record_format should be set to F. This option is incompatible with is_record_sequence. |
| .option("record_length_map", """{"A":100,"B":50}""") | Specifies a mapping between record length field values and actual record lengths. |
| .option("record_extractor", "com.example.record.extractor") | Specifies a class for parsing record in a custom way. The class must inherit RawRecordExtractor and Serializable traits. See the chapter on record extractors above. |
| .option("minimum_record_length", 1) | Specifies the minimum length a record is considered valid, will be skipped otherwise. |
| .option("maximum_record_length", 1000) | Specifies the maximum length a record is considered valid, will be skipped otherwise. |
| Option (usage example) | Description |
|---|---|
| .option("record_format", "D") | Record format from the spec. One of F (fixed length, default), FB (fixed block), V(variable length RDW),VB(variable block BDW+RDW),D` (ASCII text). |
| .option("is_text", "true") | If 'true' the file will be considered a text file where records are separated by an end-of-line character. Currently, only ASCII files having UTF-8 charset can be processed this way. If combined with record_format = D, multisegment and hierarchical text record files can be loaded. |
| Option (usage example) | Description |
|---|---|
| .option("segment_field", "SEG-ID") | Specify a segment id field name. This is to ensure the splitting is done using root record boundaries for hierarchical datasets. The first record will be considered a root segment record. |
| .option("redefine-segment-id-map:0", "REDEFINED_FIELD1 => SegmentId1,SegmentId2,...") | Specifies a mapping between redefined field names and segment id values. Each option specifies a mapping for a single segment. The numeric value for each mapping option must be incremented so the option keys are unique. |
| .option("segment-children:0", "COMPANY => EMPLOYEE,DEPARTMENT") | Specifies a mapping between segment redefined fields and their children. Each option specifies a mapping for a single parent field. The numeric value for each mapping option must be incremented so the option keys are unique. If such mapping is specified hierarchical record structure will be automatically reconstructed. This require redefine-segment-id-map to be set. |
| .option("enable_indexes", "true") | Turns on indexing of multisegment variable length files (on by default). |
| .option("input_split_records", 50000) | Specifies how many records will be allocated to each split/partition. It will be processed by Spark tasks. (The default is not set and the split will happen according to size, see the next option) |
| .option("input_split_size_mb", 100) | Specify how many megabytes to allocate to each partition/split. (The default is 100 MB) |
| Option (usage example) | Description |
|---|---|
| .option("segment_field", "SEG-ID") | Specified the field in the copybook containing values of segment ids. |
| .option("segment_filter", "S0001") | Allows to add a filter on the segment id that will be pushed down the reader. This is if the intent is to extract records only of a particular segments. |
| .option("segment_id_level0", "SEGID-ROOT") | Specifies segment id value for root level records. When this option is specified the Seg_Id0 field will be generated for each root record |
| .option("segment_id_level1", "SEGID-CLD1") | Specifies segment id value for child level records. When this option is specified the Seg_Id1 field will be generated for each root record |
| .option("segment_id_level2", "SEGID-CLD2") | Specifies segment id value for child of a child level records. When this option is specified the Seg_Id2 field will be generated for each root record. You can use levels 3, 4 etc. |
| .option("segment_id_prefix", "A_PREEFIX") | Specifies a prefix to be added to each segment id value. This is to mage generated IDs globally unique. By default the prefix is the current timestamp in form of '201811122345_'. |
| Option (usage example) | Description |
|---|---|
| .option("pedantic", "false") | If 'true' Cobrix will throw an exception is an unknown option is encountered. If 'false' (default), unknown options will be logged as an error without failing Spark Application. |
| .option("debug_layout_positions", "true") | If 'true' Cobrix will generate and log layout positions table when reading data. |
| .option("debug_ignore_file_size", "true") | If 'true' no exception will be thrown if record size does not match file size. Useful for debugging copybooks to make them match a data file. |
| .option("ascii_charset", "US-ASCII") | Specifies a charset to use to decode ASCII data. The value can be any charset supported by java.nio.charset: US-ASCII (default), UTF-8, ISO-8859-1, etc. |
| .option("field_code_page:cp825", "field1, field2") | Specifies the code page for selected fields. You can add mo than 1 such option for multiple code page overrides. |
| .option("minimum_record_length", 1) | Specifies the minimum length a record is considered valid, will be skipped otherwise. It is used to skip ASCII lines that contains invalid records, an EOF character, for example. |
| .option("maximum_record_length", 1000) | Specifies the maximum length a record is considered valid, will be skipped otherwise. |
| Option | Code page | Description |
|---|---|---|
| .option("ebcdic_code_page", "common") | Common | (Default) Only characters common across EBCDIC code pages are decoded. |
| .option("ebcdic_code_page", "cp037") | EBCDIC 037 | Australia, Brazil, Canada, New Zealand, Portugal, South Africa, USA. |
| .option("ebcdic_code_page", "cp273") | EBCDIC 273 | Germany, Austria. |
| .option("ebcdic_code_page", "cp275") | EBCDIC 275 | Brazil. |
| .option("ebcdic_code_page", "cp277") | EBCDIC 277 | Denmark and Norway. |
| .option("ebcdic_code_page", "cp278") | EBCDIC 278 | Finland and Sweden. |
| .option("ebcdic_code_page", "cp280") | EBCDIC 280 | Italy. |
| .option("ebcdic_code_page", "cp284") | EBCDIC 284 | Spain and Latin America. |
| .option("ebcdic_code_page", "cp285") | EBCDIC 285 | United Kingdom. |
| .option("ebcdic_code_page", "cp297") | EBCDIC 297 | France. |
| .option("ebcdic_code_page", "cp300") | EBCDIC 300 | Double-byte code page with Japanese and Latin characters. |
| .option("ebcdic_code_page", "cp500") | EBCDIC 500 | Belgium, Canada, Switzerland, International. |
| .option("ebcdic_code_page", "cp838") | EBCDIC 838 | Double-byte code page with Thai and Latin characters. |
| .option("ebcdic_code_page", "cp870") | EBCDIC 870 | Albania, Bosnia and Herzegovina, Croatia, Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia. |
| .option("ebcdic_code_page", "cp875") | EBCDIC 875 | A code page with Greek characters. |
| .option("ebcdic_code_page", "cp1025") | EBCDIC 1025 | A code page with Cyrillic alphabet. |
| .option("ebcdic_code_page", "cp1047") | EBCDIC 1047 | A code page containing all of the Latin-1/Open System characters. |
| .option("ebcdic_code_page", "cp1140") | EBCDIC 1140 | Same as code page 037 with € at the position of the international currency symbol ¤. |
| .option("ebcdic_code_page", "cp1141") | EBCDIC 1141 | Same as code page 273 with € at the position of the international currency symbol ¤. |
| .option("ebcdic_code_page", "cp1148") | EBCDIC 1148 | Same as code page 500 with € at the position of the international currency symbol ¤. |
| .option("ebcdic_code_page", "cp1364") | EBCDIC 1364 | Double-byte code page CCSID-1364, Korean. |
| .option("ebcdic_code_page", "cp1388") | EBCDIC 1388 | Double-byte code page CCSID-1388, Simplified Chinese. |
common_extended, cp037_extended are code pages supporting non-printable characters that converts to ASCII codes below 32.
Performance tests were performed on synthetic datasets. The setup and results are as follows.
- Spark 2.2.1
- Driver memory: 4GB
- Driver cores: 4
- Executor memory: 4GB
- Cores per executor: 1
The test Spark Application is just a conversion from the mainframe format to Parquet.
For fixed record length tests:
val sparkBuilder = SparkSession.builder().appName("Performance test")
val spark = sparkBuilder
.getOrCreate()
val copybook = "...copybook contents..."
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.load(args(0))
df.write.mode(SaveMode.Overwrite).parquet(args(1))For multisegment variable lengths tests:
val sparkBuilder = SparkSession.builder().appName("Performance test")
val spark = sparkBuilder
.getOrCreate()
val copybook = "...copybook contents..."
val df = spark
.read
.format("cobol")
.option("copybook_contents", copybook)
.option("generate_record_id", true)
.option("record_format", "V")
.option("segment_field", "SEGMENT_ID")
.option("segment_id_level0", "C")
.load(args(0))
df.write.mode(SaveMode.Overwrite).parquet(args(1))- Raw (single segment) fixed length record file
- 167 columns
- 1341 bytes per record
- 30,000,000 records
- 40 GB single input file size
- The data can be generated using
za.co.absa.cobrix.cobol.parser.examples.TestDataGen6TypeVarietygenerator app
- Multisegment variable length record file
- A copybook containing very few fields
- 68 bytes per segment 1 record and 64 bytes per segment 2 record
- 640,000,000 records
- 40 GB single input file size
- The data can be generated using
za.co.absa.cobrix.cobol.parser.examples.TestDataGen3Companiesgenerator app
- Multisegment variable length record file
- A copybook containing a segment with arrays of 2000 struct elements. This reproduces a case when a copybook contain a lot of fields.
- 16068 bytes per segment 1 record and 64 bytes per segment 2 record
- 8,000,000 records
- 40 GB single input file size
- The data can be generated using
za.co.absa.cobrix.cobol.parser.examples.generatorsTestDataGen4CompaniesWidegenerator app
sbt jacocoCode coverage will be generated on path:
{local-path}\fixed-width\target\scala-2.XY\jacoco\report\html
This is a new section where we are going to post common questions and workarounds from GitHub issues raised by our users.
Q: Numeric COMP or COMP-5 data is decoded incorrectly. Specifically, small values look like very big values
A: This is often a sign that the binary data is little-endian. Cobrix expects all binary data to be big-endian.
The workaround is to use COMP-9 (Cobrix extension) instead of COMP and COMP-5 for the affected fields.
Q: Getting the following error when using Spark < 2.4.3:
ANTLR Tool version 4.7.2 used for code generation does not match the current runtime version 4.5.3ANTLR
Runtime version 4.7.2 used for parser compilation does not match the current runtime version 4.5.321/12/20 11:42:54
ERROR ApplicationMaster: User class threw exception: java.lang.ExceptionInInitializerError
A: Option 1: Use Spark 2.4.3 or higher. Option 2: Use 'sbt assembly' as stated above in README to generate your
spark-cobol artifact tailored for your Spark version. The artifact shades ANTLR so the incompatibility should
be resolved.
Q: Getting exceptions from Hadoop classes when running Cobrix test suite on Windows:
exception or error caused a run to abort: org.apache.hadoop.io.nativeio.NativeIO$POSIX.stat(Ljava/lang/String;)Lorg/apache/hadoop/io/nativeio/NativeIO$POSIX$Stat;
java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$POSIX.stat(Ljava/lang/String;)Lorg/apache/hadoop/io/nativeio/NativeIO$POSIX$Stat;
at org.apache.hadoop.io.nativeio.NativeIO$POSIX.stat(Native Method)
at org.apache.hadoop.io.nativeio.NativeIO$POSIX.getStat(NativeIO.java:608)
A: Update hadoop dll to version 3.2.2 or newer.
-
- #719 Improve error message for fixed record length files when the record size does not divide file size.
-
- #703 Add maximum length for generated segment id fields, like
seg_id0,seg_id1, etc.
- #703 Add maximum length for generated segment id fields, like
-
- #697 Improve metadata merging method in Spark Utils. Add conflict resolution and merge flags
// Field metadata can be lost during various transformations. // You can copy metadata from one schema to another directly val df1 = ??? // A dataframe with metadata val df2 = ??? // A dataframe without metadata val mergedSchema = SparkUtils.copyMetadata(df1.schema, df2.schema) // Create the new dataframe based on the schema with merged metadata val newDf = spark.createDataFrame(df2.rdd, mergedSchema)
- #697 Improve metadata merging method in Spark Utils. Add conflict resolution and merge flags
-
- #678 Add the ability to generate Spark schema based on strict integral precision:
// `decimal(n,0)` will be used instead of `integer` and `long` .option("strict_integral_precision", "true")
- #689 Add support for '_' for hierarchical key generation at leaf level:
.option("segment_id_level0", "SEG0") // Root segment .option("segment_id_level1", "_") // Leaf segment (use 'all other' segment IDs)
- #678 Add the ability to generate Spark schema based on strict integral precision:
-
- #684 Fixed failing to read a data file in certain combination of options.
- #685 Added methods to flatten schema of a dataframe more effective than
flattenSchema(), but does not flatten arrays:// df - a DataFrame with nested structs val flatDf = SparkUtils.unstructDataFrame(df) // flatDf the same dataframe with all nested fields promoted to the top level.
-
- #666 Added support for record length value mapping.
.option("record_format", "F") .option("record_length_field", "FIELD_STR") .option("record_length_map", """{"SEG1":100,"SEG2":200}""")
- #669 Allow 'V' to be at the end of scaled PICs.
10 SCALED-DECIMAL-FIELD PIC S9PPPV DISPLAY.
- #672 Add the ability to parse copybooks with options normally passed to the
spark-cobolSpark data source.// Same options that you use for spark.read.format("cobol").option() val options = Map("schema_retention_policy" -> "keep_original") val cobolSchema = CobolSchema.fromSparkOptions(Seq(copybook), options) val sparkSchema = cobolSchema.getSparkSchema.toString() println(sparkSchema)
- #674 Extended the usage of indexes for variable record length files with a record length field.
.option("record_length_field", "RECORD-LENGTH") .option("enable_indexes", "true") // true by default so can me omitted
- #666 Added support for record length value mapping.
-
- #659 Fixed record length option when record id generation is turned on.
-
- #653 Add support for new EBCDIC code pages: 273, 500, 1140, 1141, 1148.
-
- #624 Add support for binary fields that have
PIC XandUSAGE COMP.
- #624 Add support for binary fields that have
-
- #620 Fixed a regression bug that made a breaking change to custom record extractors. The source code compatibility has been restored.
-
- #601 Fixed
file_start_offsetandfile_end_offsetoptions for VB record format (BDW+RDW). - #614 Fixed catching a state when a custom record extractor does not conform to the contract.
- #613 Added the ability of custom record extractors to get header stream pointing to teh beginning of the file.
- #607 Added
minimum_record_lengthandmaximum_record_lengthoptions.
- #601 Fixed
-
- #539 Fixed 'cp300', and added experimental support for 'cp1364' and 'cp1388' code pages (thanks @BenceBenedek).
- #590 Changed from
.option("extended_metadata", true)to.option("metadata", "extended")allowing other modes like 'basic' (default) and 'false' (disable metadata). - #593 Added option
.option("generate_record_bytes", true)that adds a field containing raw bytes of each record decoded.
-
- #576 Added the ability to create DataFrames from RDDs plus a copybook using
.Cobrix.fromRdd()extension (look for 'Cobrix.fromRdd' for examples). - #574 Added the ability to read data files with fields encoded using multiple code pages using (
.option("field_code_page:cp037", "FIELD-1,FIELD_2")). - #538 Added experimental support for
cp00300, the 2 byte Japanese code page (thanks @BenceBenedek).
- #576 Added the ability to create DataFrames from RDDs plus a copybook using
-
- #550 Added
.option("extended_metadata", true)option that adds many additional metadata fields (PIC, USAGE, etc) to the generated Spark schema. - #567 Added support for new code pages 838, 870, 1025 (Thanks @sree018).
- #569 Added support for field length expressions based on filed on the copybook See Variable length records support.
- #572 Improved performance of non-UTF8 encoded ASCII test files.
- #560 Fixed 'segment_filter' for ASCII files when Spark test splitter is used.
- #550 Added
-
- #516 Added support for unsigned packed numbers via a Cobrix extension (COMP-3U).
- #542 Added
.option("filler_naming_policy", "previous_field_name")allowing for a different filler naming strategy. - #544 Added
data_pathsoption to replacepathsoption that conflicts with Sparks internal optionpaths. - #545 Added support for
stringdebug columns for ASCII (D/D2/T) files (.option("debug", "string")). - #543 Improved performance of processing ASCII text (D/D2/T) files with variable OCCURS.
- #553 Fixed variable occurs now working properly with basic ASCII record format (D2).
- #556 Fixed
file_end_offsetoption dropping records from the end of partitions instead of end of files.
Older versions
-
- #510 Fixed dropping of FILLER fields in Spack Schema if the FILLER has OCCURS of GROUPS.
-
- #396 Added support for parsing copybooks that do not have root record GROUP.
- #423 Added
Record_Byte_Lengthfield to be generated whengenerate_record_idis set totrue. - #500 Improved null detection by default (the old behavior can be restored using
.option("improved_null_detection", "false")). - #491 Strictness of sign overpunching is now controlled by
.option("strict_sign_overpunching", "true")(false by default). When set totruesign overpunching is not allowed for unsigned fields. Whenfalse, positive sign overpunching is allowed for unsigned fields. - #501 Fixed decimal field null detection when 'improved_null_detection' is turned on.
- #502 Fixed parsing of scaled decimals that have a pattern similar to
SVP9(5).
-
- #481 ASCII control characters are now ignored instead of being replaced with spaces.
A new string trimming policy (
keep_all) allows keeping all control characters in strings (including0x00). - #484 Fix parsing of ASCII files so that only full records are parsed. The old behavior
can be restored with
.option("allow_partial_records", "true").
- #481 ASCII control characters are now ignored instead of being replaced with spaces.
A new string trimming policy (
-
- #474 Fix numeric decoder of unsigned DISPLAY format. The decoder made more strict and does not allow sign overpunching for unsigned numbers.
- #477 Fixed NotSerializableException when using non-default logger implementations (Thanks @joaquin021).
-
- #324 Allow removing of FILLERs from AST when parsing using 'parseSimple()'. The signature of the method has changed. The boolean arguments now reflect more clearly what they do.
- #466 Added
maxElementsandminElementsto Spark schema metadata for array fields created from fields withOCCURS. This allows knowing the maximum number of elements in arrays when flattening the schema.
-
- #459 Fixed signed overpunch for ASCII files.
-
- #451 Fixed COMP-9 (Cobrix extension for little-endian binary fields).
-
- #430 Added support for 'twisted' RDW headers when big-endian or little-endian RDWs use unexpected RDW bytes.
-
- #427 Fixed parsing of identifiers that end with '-' or '_'.
-
- #420 Add experimental support for fixed blocked (FB) record format.
- #422 Fixed decoding of 'broken pipe' (
¦) character from EBCDIC. - #424 Fixed an ASCII reader corner case.
-
- #412 Add support for variable block (VB aka VBVR) record format.
Options to adjust BDW settings are added:
is_bdw_big_endian- specifies if BDW is big-endian (false by default)bdw_adjustment- Specifies how the value of a BDW is different from the block payload. For example, if the side in BDW headers includes BDW record itself, use.option("bdw_adjustment", "-4").
- Options
is_record_sequenceandis_xcomare deprecated. Use.option("record_format", "V")instead. - #417 Multisegment ASCII text files have now direct support using
record_format = D.
- #412 Add support for variable block (VB aka VBVR) record format.
Options to adjust BDW settings are added:
-
- #405 Fix extracting records that contain redefines of the top level GROUPs.
- #406 Use 'collapse_root' retention policy by default. This is the breaking,
change, to restore the original behavior add
.option("schema_retention_policy", "keep_original"). - #407 The layout positions summary generated by the parser now contains level numbers for root level GROUPs. This is a breaking change if you have unit tests that depend on the formatting of the layout positions output.
-
- #397 Fix skipping of empty lines when reading ASCII files with
is_record_sequence = true - #394 Added an ability to specify multiple paths to read data from (Use
.option("paths", inputPaths.mkString(","))). This is a workaround implementation since adding support for multiple paths inload()would require a big rewrite forspark-cobolfrom data source to data format. - #372 Added an option to better handle null values in DISPLAY formatted data:
.option("improved_null_detection", "false")
- #397 Fix skipping of empty lines when reading ASCII files with
-
- #373 Added the ability to create Uber jar directly from the source code of the project (https://github.com/AbsaOSS/cobrix#creating-an-uber-jar).
-
- #146 Added support for S3 storage. The S3 support could be considered experimental since only S3A connector has been tested. However, since Cobrix is built on filesystem abstractions provided by Hadoop and Spark libraries, there shouldn't be any issues using other S3 connectors.
-
- #349 Fixed regression bug introduced in 2.1.4 resulting in an infinite loop in the sparse index generation for fixed-record length multisegment files.
-
- #334 Added support for reading multisegment ASCII text files.
- #338 Added support for custom record extractors that are better replacement for custom record header parsers.
- #340 Added the option to enforce record length:
.option("record_length", "123"). - #335 Fixed sparse index generation for files that have variable length occurs, but no RDWs.
- #342 Fixed sparse index generation for files with multiple values of the root segment id.
- #346 Updated Spark dependency to 2.4.7 (was 2.4.5).
-
- #329 Added debug fields generation for redefines (Thanks @codealways).
-
- #53 Added an option to retain FILLERs.
.option("drop_value_fillers", "false"). Use together with.option("drop_group_fillers", "false"). - #315 Added
CopybookParser.parseSimple()that requires only essential arguments. - #316 Added support for copybooks that contain non-breakable spaces (0xA0) and tabs.
- #53 Added an option to retain FILLERs.
-
- #291 Added ability to generate debug fields in raw/binary format.
- #295 Added
is_textoption for easier processing ASCII text files that uses EOL characters as record separators. - #294 Updated Spark compile-time dependency to
2.4.5to remove security alerts. - #293 Copybook-related error messages are made more clear.
-
- #273 Fixed the class loader for custom code pages (Thanks @schaloner-kbc).
-
- #273 Fixed the class loader for custom code pages (Thanks @schaloner-kbc).
-
- #239 Added support for generation of debugging fields (
.option("debug", "true")). - #249 Added support for NATIONAL (
PIC N) formatted strings (Thanks @schaloner-kbc).
- #239 Added support for generation of debugging fields (
-
- #239 Added an ability to load files with variable size OCCURS and no RDWs.
- #249 Fixed handling of variable size OCCURS when loading hierarchical files.
- #251 Fixed hidden files not being ignored when there are many files in a directory.
- #252 Fixed compatibility of 'with_input_file_name_col' with file offset options.
-
- #241 Fixed EBCDIC string to number converter to support comma as the decimal separator.
-
- #241 Added support for comma as a decimal seprartor for explicit decimal point in DISPLAY numeric format (Thanks @tr11).
-
- #225 Added
.option("ascii_charset", chrsetName)to specify a charset for ASCII data. - #221 Added
.option("with_input_file_name_col", "file_name")for variable length files to workaround empty value returned byinput_file_name(). - Fixed Scala dependency in artifacts produced by
sbt. The dependency is now provided so that a fat jar produced withspark-coboldependency is compatible to wider range of Spark deployments.
- #225 Added
-
- Added cross-compilation for Scala
2.11and2.12viasbtbuild (Thanks @GeorgiChochov). - Added sbt build for the example project.
- Added cross-compilation for Scala
-
- This is the last
Mavenrelease. New versions are going to be released viasbtand cross-compiled for Scala2.11and2.12. - Fixed too permissive parsing of uncompressed (DISPLAY) numbers.
- This is the last
-
- Fixed processing files that have special characters in their paths.
-
- Added an option (
segment-children) to reconstruct hierarchical structure of records. See Automatic reconstruction of hierarchical structure
- Added an option (
-
- Fixed trimming of VARCHAR fields.
-
- Added an option to control behavior of variable size 'OCCURS DEPENDING ON' fields (see
.option("variable_size_occurs", "true")).
- Added an option to control behavior of variable size 'OCCURS DEPENDING ON' fields (see
-
- The parser is completely rewritten in ANTLR by Tiago Requeijo. This provides a lot of benefits, including but not limited to:
- This should provide more strict compliance to the COBOL spec.
- The parser should now be more robust and maintainable.
- Changes to the parser from now on should be less error prone.
- Syntax error messages should be more meaningful.
- The parser is completely rewritten in ANTLR by Tiago Requeijo. This provides a lot of benefits, including but not limited to:
-
- Added ability to control truncation of comments when parsing a copybook. Historically the first 6 bytes of a copybook are ignored, as well as all characters after position 72. Added options to control this behavior.
- Added unit tests for the copybook parser covering several exotic cases.
-
- Added ability to read variable length files without RDW. A length field and [optionally] offsets can be specified instead.
- Added support for VARCHAR fields at the end of records. Length of such string fields is determined by the length of each record.
- Added support for segment id to redefine fields mapping for fixed-record length files.
-
- Added support for IBM floating point formats.
- Fixed sparse index generation when file header has the same segment id as root record.
- Fixed sparse index generation when a file does not contain any data, but just headers and footers.
-
- Make
peadntic=falseby default so existing workflows won't break. - Use
cobrix_build.propertiesfile for storing Cobrix version instead ofbuild.propertiesto avoid name clashes.
- Make
-
- Added options to adjust record sizes returned by RDW headers. RDWs may or may not include themselves as part of record size.
- Added tracking of unrecognized and redundant options. If an option to
spark-cobolis unrecognized or redundant the Spark Application won't run unlesspedantic = false. - Added logging of Cobrix version during Spark Application execution.
- Improved custom record header parser to support wider range of use cases.
- Fixed processing paths that contain wildcards.
- Various improvements in the structure of the project, POM files and examples.
-
- This is a minor feature release.
- Added support for specifying several copybooks. They will be automatically merged into a larger one (Thanks Tiago Requeijo).
- Added an option for ignoring file headers and footers ('file_start_offset', 'file_end_offset').
- Added the dropRoot and restrictTo operations for the copybooks parser (Thanks Tiago Requeijo).
- Added support for explicit decimal point location together with scale factor (Thanks @gd-iborisov)
- Added an option to suppress file size check for debugging fixed record length files ('debug_ignore_file_size').
- Added support for sparse indexes when fixed record length files require variable record length features. It can be, for example, when a record id generation is requested or when file offsets are used.
- Improved custom record header parser interface:
- Cobrix now provides file offset, size and record number to record metadata parser.
- Fixed handling the case where record headers are part of the copybook.
-
- This is a minor feature release.
- Cobrix now handles top level REDEFINES (Thanks Tiago Requeijo).
- Added support for extracting non-terminal fields (GROUPs) as string columns (Thanks Tiago Requeijo). (see 'non_terminals' option)
- Added support for decimal scale 'P' in PICs.
- Added ability to specify custom record header parsers for variable record length files (see 'record_header_parser' option)
- Interpret Decimal values with no fractional digits as Integral (Thanks Tiago Requeijo).
- Fixed OCCURS depending on non-integer fields (Thanks Tiago Requeijo).
- Fixed code duplication in AST traversal routines (Thanks Tiago Requeijo).
- Fixed handling number PICs that start with implicit decimal point (e.g. 'SV9(5)')
- Fixed unit tests failure when built in Windows
-
- This is a minor feature release.
- Added ability for a user to provide a custom EBCDIC code page to Unicode conversion table.
- Fixed generated record id and 'segment id fields inconsistencies.
-
- This is a minor feature release.
- Added an option to specify if and how strings should be trimmed.
- Added an option to select an EBCDIC code page with support of non-printable characters.
-
- This is a minor feature release.
- Added ability to specify segment id to redefine mapping. If specified Cobrix won't parse redefines that are not valid for a given segment id. This should increase performance when parsing multisegment files.
- Unsigned numeric type patterns are now handled more strictly resulting in
nullvalues if a number is decoded as negative
-
- This is a hotfix release.
- Fixed segment id filter pushdown if the segment id field contains non-decodable values
-
- This is a minor feature release.
- Added support for big endian RDW headers in record sequence files
option("is_rdw_big_endian", "true"). By default RDW headers are expected to be little-endian (for compatibility with earlier versions of Cobrix). - Improved default settings for sparse index generation to achieve better data locality.
- Fixed parsing field names containing 'COMP-' inside its name.
-
- This is a maintenance release.
- Added support for PICs specifying separate sign character (for example,
9(4)+). - Added support for PICs characters specifying zeros masking (for example,
Z(4)). - Added support for reading ASCII data files using
option("encoding", "ascii").
-
- This is a minor feature release. There are changes that change behavior.
- Added balancing partitions among potentially idle executors. See the section on locality optimization.
- Added performance charts to README.md
- Added 2 standalone projects in the 'examples' folder. It can be used as templates for creating Spark jobs that use Cobrix
- Added option to drop all FILLER GROUPs. See section "Group Filler dropping" describing the new option.
- Fixed handling of GROUP fields having only FILLER nested fields. Such groups are removed automatically because some formats don't support empty struct fields.
- All parser offsets are now in bytes instead of bits.
- COMP-5 is similar to COMP-4, the truncation happens by the size of the binary data, not exactly by PIC precision specification. For COMP-5 numbers are expected to be big endian.
- Added artificial COMP-9 format for little endian binary numbers.
-
- Added partitioning at the record-level by leveraging HDFS locations. This makes Cobrix data locality aware data source.
- Parser decoders have been rewritten resulting in about 20% increase of performance
- Added support for IEEE-754 floating point numbers for COMP-1 and COMP-2
- Added support for decimal numbers with explicit decimal point
- Added support for DISPLAY-formatted numbers sign overpunching
- Added support for DISPLAY-formatted numbers sign separate (both leading and trailing)
- Added support for very big numbers (when precision is bigger than 18)
- Added ability to filter by several segment ids in a multiple segment file (Thanks Peter Moon)
- Syntax check made more strict, added more diagnostic messages.
- The "is_xcom" option is renamed to "is_record_sequence" since other tools provide such a header as well. The old option remains for compatibility.
| Option (usage example) | Description |
|---|---|
| .option("is_record_sequence", "true") | Specifies that input files have byte record headers. |
-
- Fixed file retrieval by complying with HDFS patterns and supporting glob patterns.
- Increased performance of BINARY/COMP formatted numbers by rewriting binary number decoders.
- Made partition split by size more accurate, make it the default split type
- Aligned input split terminology according to other Spark data sources (see the table below)
- When both split options are specified 'input_split_records' is used
- When none split of the split options are specified the default is split by size of 100 MB
| Option (usage example) | Description |
|---|---|
| .option("input_split_records", 50000) | Specifies how many records will be allocated to each split/partition. It will be processed by Spark tasks. (The default is 50K records) |
| .option("input_split_size_mb", 100) | Specify how many megabytes to allocate to each partition/split. (The default is 100 MB) |
-
- Added an index generation for multisegment variable record size files to make the reader scalable.
- It is turned on by default, you can restore old behaviour using .option("enable_indexes", "false")
- Added options to control index generation (first table below).
- Added generation of helper fields for hierarchical databases (second table below). These helper fields allows to split a dataset into individual segments and then join them. The helper fields will contain segment ids that can be used for joining the resulting tables. See the guide on loading hierarchical data sets above.
- Fixed many performance issues that should make reading mainframe files several times faster. The actual performance depends on concrete copybooks.
- Added an index generation for multisegment variable record size files to make the reader scalable.
- Thanks to the following people the project was made possible and for all the help along the way:
- Andrew Baker, Francois Cillers, Adam Smyczek, Jan Scherbaum, Peter Moon, Clifford Lategan, Rekha Gorantla, Mohit Suryavanshi, Niel Steyn
- Thanks to Tiago Requeijo, the author of the current ANTLR-based COBOL parser contributed to Cobrix.
- Thanks to the authors of the original COBOL parser. When we started the project we had zero knowledge of COBOL and this parser was a good starting point:
- Ian De Beer, Rikus de Milander (https://github.com/zenaptix-lab/copybookStreams)
Companies, Names, Ids and values in all examples present in this project/repository are completely fictional and were generated randomly. Any resemblance to actual persons, companies or actual transactions is purely coincidental.
Take a look at other COBOL-related open source projects. If you think a project belongs in the list, please let us know, we will add it.
- RCOBOLDI - R COBOL DI (Data Integration) Package: An R package that facilitates the importation of COBOL CopyBook data directly into the R Project for Statistical Computing as properly structured data frames.