master

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 (including "flattened" nested names)

• 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

You can link against this library in your program at the following coordinates:

groupId: za.co.absa.cobrix
artifactId: spark-cobol_2.11
version: 2.1.5

groupId: za.co.absa.cobrix
artifactId: spark-cobol_2.12
version: 2.1.5

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_2.11:2.1.5

$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.12:2.1.5

This repository contains several standalone example applications in examples/spark-cobol-app directory. It is a Maven project that contains several examples:

• SparkTypesApp is 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.
• SparkCobolApp is an example of a Spark Job for handling multisegment variable record length mainframe files.
• SparkCodecApp is 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 4
• SparkCobolHierarchical is 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.

1. Create a Spark SQLContext

2. Start a sqlContext.read operation specifying za.co.absa.cobrix.spark.cobol.source as the format

3. 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 adding file:// 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...").

4. Inform the path to the HDFS directory containing the files: ... .load("path_to_directory_containing_the_binary_files")

5. 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").

1. Create a Spark StreamContext

2. Import the binary files/stream conversion manager: za.co.absa.spark.cobol.source.streaming.CobolStreamer._

3. Read the binary files contained in the path informed in the creation of the SparkSession as a stream: ... streamingContext.cobolStream()

4. Apply queries on the stream: ... stream.filter("some_filter") ...

5. 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_2.11-2.1.5.jar
• cobol-parser_2.11-2.1.5.jar
• scodec-core_2.11-1.10.3.jar
• scodec-bits_2.11-1.1.4.jar
• antlr4-runtime-4.7.2.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_2.11:2.1.5
or 
$ spark-shell --master yarn --deploy-mode client --driver-cores 4 --driver-memory 4G --jars spark-cobol_2.11-2.1.5.jar,cobol-parser_2.11-2.1.5.jar,scodec-core_2.11-1.10.3.jar,scodec-bits_2.11-1.1.4.jar,antlr4-runtime-4.7.2.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 of the dependencies.

Creating an uber jar for Cobrix is very easy. Just go to a folder with one of the examples, say, examples/spark-cobol-app. After that run mvn package. Then, copy target/spark-cobol-app-0.0.1-SNAPSHOT.jar to a clustrr and run:

$ spark-shell --jars spark-cobol-app-0.0.1-SNAPSHOT.jar

Our example pom projects are set up so an uber jar is built every time you build it using Maven.

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:

import za.co.absa.cobrix.cobol.parser.CopybookParser
import za.co.absa.cobrix.spark.cobol.schema.{CobolSchema, SchemaRetentionPolicy}

val parsedSchema = CopybookParser.parseTree(copyBookContents)
val cobolSchema = new CobolSchema(parsedSchema, SchemaRetentionPolicy.CollapseRoot)
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.parseTree(copyBookContents)
println(copyBook.generateRecordLayoutPositions())

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.

To load variable length record file the following option should be specified:

.option("is_record_sequence", "true")

The space used by the headers should not be mentioned in the copybook if this option is used. Please refer to the 'Record headers support' section below.

Mainframe data often contain only one root GROUP. In such cases such a GROUP can be considered something similar to XML rowtag. Cobrix allows to collapse the GROUP and expand it's records. To turn this on use the following option:

.option("schema_retention_policy", "collapse_root")

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.

Normally Spark schema for such a copybook will look like this:

root
 |-- RECORD: struct (nullable = true)
 |    |-- 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 "collapse_root" the root group will be collapsed and the schema will look like this (note the RECORD field is no longer present):

root
 |-- 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" and "Record_Id" 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)

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("is_record_sequence", "true")

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 'is_record_sequence' 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 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:

    // A new experimental way
    val df = spark
      .read
      .format("cobol")
      .option("copybook_contents", copybook)
      .option("is_text", "true")
      .option("encoding", "ascii")
      .option("schema_retention_policy", "collapse_root")
      .load(tmpFileName)

Working example 2:

    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("schema_retention_policy", "collapse_root")
  .option("is_record_sequence", "true")
  .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 the 'is_record_sequence' option should be set to 'true'.

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("schema_retention_policy", "collapse_root")     // Collapses the root group returning it's field on the top level of the schema
  .option("is_record_sequence", "true")
  .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("schema_retention_policy", "collapse_root")
  .option("is_record_sequence", "true")

  // 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("schema_retention_policy", "collapse_root")
  .option("is_record_sequence", "true")
  .option("segment_field", "SEGMENT_ID")
  .option("segment_id_level0", "C")
  .option("segment_id_level1", "P")
  .load("examples/multisegment_data/COMP.DETAILS.SEP30.DATA.dat")

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

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)
      .option("schema_retention_policy", "collapse_root")
      .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("schema_retention_policy", "collapse_root")
      .option("is_record_sequence", "true")
      .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.TestDataGen6TypeVariety generator 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.TestDataGen3Companies generator 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.generatorsTestDataGen4CompaniesWide generator app

• 2.1.5 released 11 December 2020.

  • #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.

• 2.1.4 released 4 December 2020.

  • #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).

• 2.1.3 released 11 November 2020.

  • #329 Added debug fields generation for redefines (Thanks @codealways).

• 2.1.2 released 2 November 2020.

  • #325 Added support for EBCDIC code page 875 (Thanks @vbarakou).

• 2.1.1 released 18 August 2020.

  • #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.

• 2.1.0 released 11 June 2020.

  • #291 Added ability to generate debug fields in raw/binary format.
  • #295 Added is_text option for easier processing ASCII text files that uses EOL characters as record separators.
  • #294 Updated Spark compile-time dependency to 2.4.5 to remove security alerts.
  • #293 Copybook-related error messages are made more clear.

• 2.0.8 released 14 May 2020.

  • #184 Record extractors are made generic to be reusable for other targets in addition to Sspark Row. (Thanks @tr11).
  • #283 Added a custom JSON parser to mitigate jackson compatibility when spark-cobol is used in Spark 3.0. (Thanks @tr11).

• 2.0.7 released 14 April 2020.

  • #273 Fixed the class loader for custom code pages (Thanks @schaloner-kbc).

• 2.0.7 released 14 April 2020.

  • #273 Fixed the class loader for custom code pages (Thanks @schaloner-kbc).

• 2.0.6 released 6 April 2020.

  • #151 Added an option (occurs_mapping) to define mappings between non-numeric fields and sizes of corresponding OCCURS (Thanks @tr11).
  • #269 Added support for segment redefines deeply nested, instead of requiring them to be defined always at the top record level.

• 2.0.5 released 23 March 2020.

  • #239 Added support for generation of debugging fields (.option("debug", "true")).
  • #249 Added support for NATIONAL (PIC N) formatted strings (Thanks @schaloner-kbc).

• 2.0.4 released 25 February 2020.

  • #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.

• 2.0.3 released 5 February 2020.

  • #241 Fixed EBCDIC string to number converter to support comma as the decimal separator.

• 2.0.2 released 2 February 2020.

  • #241 Added support for comma as a decimal seprartor for explicit decimal point in DISPLAY numeric format (Thanks @tr11).

• 2.0.1 released 20 December 2019.

  • #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 by input_file_name().
  • Fixed Scala dependency in artifacts produced by sbt. The dependency is now provided so that a fat jar produced with spark-cobol dependency is compatible to wider range of Spark deployments.

• 2.0.0 released 11 December 2019.

  • Added cross-compilation for Scala 2.11 and 2.12 via sbt build (Thanks @GeorgiChochov).
  • Added sbt build for the example project.

• 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.