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Cobrix - COBOL Data Source for Apache Spark

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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.

Motivation

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

Features

  • 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

Videos

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

Requirements

spark-cobol Spark
0.x 2.2+
1.x 2.2+
2.x 2.4.3+

Linking

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

Scala 2.11

Maven Central

groupId: za.co.absa.cobrix
artifactId: spark-cobol_2.11
version: 2.0.1

Scala 2.12

Maven Central

groupId: za.co.absa.cobrix
artifactId: spark-cobol_2.12
version: 2.0.1

Using with Spark shell

This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell:

Spark compiled with Scala 2.11

$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.11:2.0.1

Spark compiled with Scala 2.12

$SPARK_HOME/bin/spark-shell --packages za.co.absa.cobrix:spark-cobol_2.12:2.0.1

Linking legacy spark-cobol

Maven Central

groupId: za.co.absa.cobrix
artifactId: spark-cobol
version: 1.1.2

Usage

Quick start

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.

Reading Cobol binary files from HDFS/local and querying them

  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").

Streaming Cobol binary files from a directory

  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()

Using Cobrix from a Spark shell

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.

Getting all Cobrix 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_2.11-2.0.1.jar
  • cobol-parser_2.11-2.0.1.jar
  • scodec-core_2.11-1.10.3.jar
  • scodec-bits_2.11-1.1.4.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_2.11:2.0.1
or 
$ spark-shell --master yarn --deploy-mode client --driver-cores 4 --driver-memory 4G --jars spark-cobol_2.11-2.0.1.jar,cobol-parser_2.11-2.0.1.jar,scodec-core_2.11-1.10.3.jar,scodec-bits_2.11-1.1.4.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 2.4.4
      /_/

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>

Creating an uber jar

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.

Other Features

Spark SQL schema extraction

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())

Variable length records support

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.

Schema collapsing

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

Record Id fields generation

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)

Locality optimization for variable-length records parsing

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)

Workload optimization for variable-length records parsing

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.

Record headers support

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 headers parser

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")

Reading ASCII text file

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:

    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.parseTree(ASCII(), copybook, dropGroupFillers = false, segmentRedefines = Seq(), stringTrimmingPolicy = StringTrimmingPolicy.TrimNone, ebcdicCodePage = CodePage.getCodePageByName("common"), nonTerminals = Seq())
    val cobolSchema = new CobolSchema(parsedCopybook, SchemaRetentionPolicy.CollapseRoot, false)
    val sparkSchema = cobolSchema.getSparkSchema


    val rddText = spark.sparkContext.textFile("src/main/resources/mini.txt")

    val rddRow = rddText.map(str => {
      RowExtractors.extractRecord(parsedCopybook.ast, str.getBytes(), 0, SchemaRetentionPolicy.CollapseRoot)
    })

    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.

Automatic segment redefines filtering

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.

Group Filler dropping

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")

Reading hierarchical data sets

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.

Spark App:

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")

Schema

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)

Data sample

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.

Automatic reconstruction of hierarchical record structure

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.

Manual reconstruction of hierarchical structure

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.

Getting the first segment

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.

Getting 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.

Generating segment ids

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:

Segment 1 (Companies)
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|
+--------------------+----------+-------------+-------------------------+--------+
Segment 2 (Contacts)
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.

Joined datasets

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

Summary of all available options

File reading options
Option (usage example) Description
.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.
Copybook parsing options
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.
Data parsing options
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.
.option("ebcdic_code_page", "common") Specifies a code page for EBCDIC encoding. Currently supported values: common (default), common_extended, cp037, cp037_extended. *_extended code pages supports non-printable characters that converts to ASCII codes below 32.
.option("ebcdic_code_page_class", "full.class.specifier") Specifies a user provided class for a custom code page to UNICODE conversion.
.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("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.
Modifier options
Option (usage example) Description
.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' and 'Record_Id' fields. This is used for processing record order dependent data.
.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 files use input_file_name().
Variable record length files options
Option (usage example) Description
.option("is_record_sequence", "true") If 'true' the parser will look for 4 byte RDW headers to read variable record length files.
.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).
.option("rdw_adjustment", 0) If there is a mismatch between RDW and record length this option can be used to adjust the difference.
.option("record_length_field", "RECORD-LEN") Specifies a record length field 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. This option is incompatible with is_record_sequence.
.option("record_header_parser", "com.example.record.header.parser") Specifies a class for parsing custom record headers. The class must inherit RecordHeaderParser and Serializable traits.
.option("rhp_additional_info", "") Passes a string as an additional info parameter passed to a custom record header parser (RHP). A custom RHP can get that additional info by overriding onReceiveAdditionalInfo()
Multisegment files options
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("allow_indexing", "true") Turns on indexing of multisegment variable length files (on by default).
.option("records_per_partition", 50000) Specifies how many records will be allocated to each partition. It will be processed by Spark tasks.
.option("partition_size_mb", 100) Specify how many megabytes to allocate to each partition. This overrides the above option.
Helper fields generation options
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_'.
Debug helper options
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_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.

Performance Analysis

Performance tests were performed on synthetic datasets. The setup and results are as follows.

Cluster setup

  • Spark 2.2.1
  • Driver memory: 4GB
  • Driver cores: 4
  • Executor memory: 4GB
  • Cores per executor: 1

Test Applications

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))

Performance Test 1. Fixed record length raw file

  • 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


Performance Test 2. Multisegment narrow file

  • 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


Performance Test 3. Multisegment wide file

  • 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


Changelog

  • 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.
  • 1.1.2 released 28 November 2019.

    • This is the last Maven release. New versions are going to be released via sbt and cross-compiled for Scala 2.11 and 2.12.
    • Fixed too permissive parsing of uncompressed (DISPLAY) numbers.
  • 1.1.1 released 15 November 2019.

    • Fixed processing files that have special characters in their paths.
  • 1.1.0 released 7 November 2019.

  • 1.0.2 released 21 October 2019.

    • Fixed trimming of VARCHAR fields.
  • 1.0.1 released 5 September 2019.

    • Added an option to control behavior of variable size 'OCCURS DEPENDING ON' fields (see .option("variable_size_occurs", "true")).
  • 1.0.0 released 29 August 2019.

    • 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.
  • 0.5.6 released 21 August 2019. This release should be functionally equivalent to 1.0.0.

    • 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.
  • 0.5.5 released 15 August 2019

    • 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.
  • 0.5.4 released 23 July 2019

    • 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.
  • 0.5.3 released 15 July 2019

    • Make peadntic=false by default so existing workflows won't break.
    • Use cobrix_build.properties file for storing Cobrix version instead of build.properties to avoid name clashes.
  • 0.5.2 released 12 July 2019

    • 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-cobol is unrecognized or redundant the Spark Application won't run unless pedantic = 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.
  • 0.5.1 released 26 June 2019

    • 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.
  • 0.5.0 released 17 May 2019

    • 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
  • 0.4.2 released 29 Mar 2019

    • 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.
  • 0.4.1 released 15 Mar 2019

    • 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.
  • 0.4.0 released 6 Mar 2019

    • 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 null values if a number is decoded as negative
  • 0.3.3 released 21 Feb 2019

    • This is a hotfix release.
    • Fixed segment id filter pushdown if the segment id field contains non-decodable values
  • 0.3.2 released 14 Feb 2019

    • 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.
  • 0.3.1 released 11 Jan 2019

    • 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").
  • 0.3.0 released 17 Dec 2018

    • 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.
  • 0.2.11 released 10 Dec 2018

    • 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.
  • 0.2.10 released 26 Nov 2018

    • 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)
  • 0.2.9 released 21 Nov 2018

    • 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("allow_indexing", "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.

Acknowledgements

  • 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:

Disclaimer

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.

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