Meta Integration® Model Bridge (MIMB)
"Metadata Integration" Solution

MIMB Bridge Documentation

MIMB Import Bridge from Apache Kafka File System (API and Schema Registry)

Bridge Specifications

Vendor Apache
Tool Name Kafka File System
Tool Version Kafka 2.x
Tool Web Site http://kafka.apache.org/
Supported Methodology [File System] Multi-Model, Data Store (NoSQL / Hierarchical, Physical Data Model) via Java API

SPECIFICATIONS
Tool: Apache / Kafka File System version Kafka 2.x via Java API
See http://kafka.apache.org/
Metadata: [File System] Multi-Model, Data Store (NoSQL / Hierarchical, Physical Data Model)
Component: ApacheKafka version 11.0.0

DISCLAIMER
This bridge requires internet access to download third party libraries:
- such as https://repo.maven.apache.org/maven2/ to download open source third party libraries,
- and more sites for other third party software such as database specific jdbc drivers.

The downloaded third party libraries are stored into $HOME/data/download/MIMB/
- If https fails, the bridge then tries with http.
- If a proxy is used to access internet, you must configure that proxy in the JRE (see the -j option in the Miscellaneous parameter).
- If the bridge does not have full access to internet, that $HOME/data/download/MIMB/ directory can be copied from another server with internet access where the command $HOME/bin/MIMB.sh (or .bat) -d can be used to download all third party libraries used by all bridges at once.

By running this bridge, you hereby acknowledge responsibility for the license terms and any potential security vulnerabilities from these downloaded third party software libraries.

OVERVIEW
This bridge crawls a data lake implemented on Apache Kafka to detect (reverse engineer) metadata from all the files (for data catalog purpose). This includes data sampling driven metadata discovery of the data structure (e.g. CSV table, JSON hierarchy) and data types (e.g. Integer, Date, String).
Just like any bridge from file systems like HDFS, this bridge supports any files like CSV, JSON, Avro, Parquet. However in Kafka, the files are organized in topics instead of partitions.

The primary use case of Kafka is for high-performance data pipelines from messaging queue to a full-fledged event streaming platform. In such case, all topics are exclusively composed of Avro files with metadata descried in a (Confluent) Kafka registry.
When the "Schema Registry URL" parameter is specified, this bridge automatically retrieves the metadata of that topic from the Kafka registry.

FREQUENTLY ASKED QUESTIONS
When connecting to Kafka using:
"PLAIN authentication"
Specify 'JAAS configuration path' and leave empty 'Kafka brokers principal name' parameter.

"KERBEROS authentication"
Specify specify values of both parameters.

"without authentication"
Leave both of these parameters empty.

Please refer to the individual parameter's documentation for more details.

LIMITATIONS
Refer to the current general known limitations at http://metaintegration.com/Products/MIMB/MIMBKnownLimitations.html or bundled in Documentation/ReadMe/MIMBKnownLimitations.html
When you run both Kafka cluster (server) version 1.1.x and the bridge (client) on Windows systems the import could fail with a timeout error. The Kafka version 2.0.x resolved the issue.

SUPPORTED FILES
Data Definition / Schema / Metadata file formats (no data):
- Fixed Width files typically from mainframe (see details below)
- COBOL COPYBOOK files typically from mainframe (see details below)
- W3C XML XSD (XML Schema Definition)

Text data file formats (data sampling driven metadata discovery):
- Delimited (Flat) files such as CSV (see details below)
- Open Office Excel XML .XSLX (see details below)
- W3C XML (not defined from XML XSD)
- JSON (JavaScript Object Notation) (see details below)

Binary data file formats (which include a schema definition as header or footer):
- Apache Avro (see details below)
- Apache Parquet (see details below)
- Apache ORC (see details below)

as well as the compressed versions of the above formats:
- ZIP (as a compression format, not as archive format)
- BZIP
- GZIP
- LZ4
- Snappy (as standard Snappy format, not as Hadoop native Snappy format)

DELIMITED FILES
This bridge detects (reverse engineer) the metadata from a data file of type Delimited File (also known as Flat File).
The detection of such Delimited File is not based on file extensions (such as .CSV, .PSV) but rather by sampling the file content.

The bridge can detect a header row, and use it to create the field name, otherwise generic field names are created.

The bridge samples up to 100 rows in order to automatically detect the field separators which by default include:
', (comma)' , '; (semicolon)', ': (colon)', '\t (tab)', '| (pipe)', '0x1 (ctrl+A)', 'BS (\u0008)'
More separators can be added in the auto detection process (including double characters), see the Miscellaneous parameter.

During the sampling, the bridge also detects the file data types, such as DATE, NUMBER, STRING.

FIXED WIDTH FILES
This bridge creates metadata for data files of type Fixed Width File.
Such metadata cannot be automatically detected (reverse engineered) by sampling the data files (e.g. customers.dat or even just customers with no extension).
Therefore, this bridge imports a 'Fixed Width File Definition' file which must be with extension .fixed_width_file_definition format file
(e.g. customers.dat.fixed_width_file_definition format file will create the metadata of a file named file customers with the fields defined inside)
This is the equivalent of a RDBMS DDL for fixed width files. With such a long extension, this data definition file can coexist with the actual data files in the each file system directory containing them.

The 'Fixed Width File Definition' file format is defined as follows:
- Format file must start with the following header:
column name, offset, width, data type, comment
- All offsets must be unique and greater than or equal to 0.
a,0
b,4
- The file format is invalid when some columns have offsets and others don't.
a,0
b,
c,4
- When all columns do not have offsets but have widths the application assumes that columns are ordered and calculates offsets based on widths.
a,,4 -> a,1,4
b,,25 -> b,5,25
- When the offset is present the application uses widths for documentation only.
a,1,4
b,5,25
- Types and comments are used as documentation only.
a,1,4,int
b,5,25,char[25],identifier

This bridge detects the following data types: INTEGER, FLOAT, STRING, DATE, BOOLEAN.

COBOL COPYBOOK FILES
This bridge can only import the COBOL COPYBOOK files (which contain the data definitions), therefore does not detect (reverse engineer) metadata from actual COBOL data files.
The detection of such COBOL COPYBOOK file is not based on file extensions (such as .CPY) but rather by sampling the file content.

This bridges creates a 'Physical Hierarchical Model' which reflects a truly flat, byte-position defined, record structure, which is useful for stitching to the DI/ETL processes. Therefore, the physical model has all the physical elements required to define a flat record, which is ONE table with all the elements (including multiple columns for OCCURS elements when the proper bridge parameter is set).

Note that this bridge does not currently support the COPY verb, and reports a parsing error at the line and position at which the COPY statement begins. In order to import Copybooks with the Copy Statement, create an expanded Copybook file with the included sections already in place (replacing the COPY verb). Most COBOL compilers have the option to output only the preprocessed Copybooks with the COPY and REPLACE statements expanded.

Frequently Asked Questions:
Q: Why is the default start column '6' (six) and the default end column '72' (seventy-two)?
A: The bridge parser counts columns starting at 0 (zero), rather than 1 (one). Thus, the defaults leave the standard first six columns for line numbers, next column for comment indicators, and last 8 columns (out of 80) for additional line comment information.

EXCEL (XLSX) FILES
This bridge detects (reverse engineer) the metadata from a data file of type Excel XML format (XLSX).
The detection of such Excel file is based on file extension .XLSX.

The bridge can detect a header row, and use it to create the field name, otherwise generic field names are created.

The bridge samples up to 1000 rows to detect the file data types, such as DATE, NUMBER, STRING.

If an Excel file has multiple sheets, each sheet is imported as the equivalent of a file/table with the same sheet name.

The bridge uses the machine's local to read files and allows you to specify the character set encoding files use.

W3C XML FILES
This W3C XML import bridge is used in conjunction with other file import bridges (e.g. CSV, XLSX, Json, Avro, Parquet) by all data lake / file crawler import bridges (e.g. File systems, Amazon S3, Hadoop HDFS).

The purpose of this XML import is to reverse engineer a model/schema from its content, when such XML was not formally defined by an XML Schema (XSD or DTD).
Such XML files are common from IoT devices uploaded into a data lake.

Nevertheless, such XML files are expected to be fully W3C compliant, especially with respect to the XML text declaration, well-formed parsed entities, and character encoding of entities.
See W3C standards for more details:
https://www.w3.org/TR/xml/#sec-TextDecl

Warning, you must use the dedicated XML based import bridges for all other needs such as:
- other standard W3C XML import bridges (e.g. DTD, XSD, WSDL, OWL/RDL)
- tool specific XML import bridges (e.g. Erwin Data Modeler XML, Informatica PowerCenter XML)

JSON FILES
This bridge imports metadata from JSON files using the Java API.
This bridge loads the entire JSON file using a streaming parser, therefore there are no size limits, although it may take time if it is a remote large JSON file.
This bridge extracts the metadata (JSON hierarchical structure) and detects the following standard JSON data types:
as defined in [https://www.json.org/
- String {"stringSample" : "some text", "stringDateSample" : "Thu Apr 06 2017 09:41:51 GMT+0300 (FLE Standard Time)", "expStringSample" : "2.99792458e8"}
- Number {"expNumberSample": 2.99792458E8, "numberSample": 3, "floatSample": 3.141592653589793}
- Array {"arraySample": [1,2,3]}
- True {"booleanSample": true}
- False {"booleanSample": false}
- Null {"nullSample": null}

In addition, the following implementation specific data types are supported:
MongoDB extension:
- The identifier {"_id": {"$oid": "50a9c951300493f64fbffdb6"}}
- Date {"dateExample" : { "$date" : "2014-01-01T05:00:00.000Z"}}
- POSIX date {"isoDateExample" : { "$date" : 1491461103897 }}
- Timestamp {"timestampExample" : { "$timestamp" : { "t" : 1412180887, "i" : 1 } }}
- Number {"numberLongExample": {"$numberLong": "7494814965"}}

CouchDB extension:
- The identifier {"_id":"someId","_rev":"1232343467"}

APACHE AVRO FILES
This bridge imports metadata from Avro files using a Java API.
Note that this bridge is not performing any data driven metadata discovery, but instead reading the schema definition at the header (top) of the Avro file.

This bridge detects the following standard Avro data types:
https://avro.apache.org/docs/current/spec.html#schema_primitive

null - no value.
boolean - a binary value.
int - a 32-bit signed integer.
long - a 64-bit signed integer.
float - a single precision (32 bit) IEEE 754 floating-point number.
double - double precision (64-bit) IEEE 754 floating-point number.
bytes - sequence of 8-bit unsigned bytes.
string - Unicode character sequence.

APACHE PARQUET FILES
This bridge imports metadata from Parquet files using a Java API.
Note that this bridge is not performing any data driven metadata discovery, but instead reading the schema definition at the footer (bottom) of the Parquet file. Therefore, this bridge needs to load the entire Parquet file to reach the schema definition at the end.

If the Parquet file is not compressed, there are no file size limit as the bridge automatically skips the data portion until the footer (although this may take time on large Parquet files). However, if the Parquet file is compressed, then the bridge needs to download the entire file to uncompress it to start with. Therefore, in such case, there is a default file size limit of 10 MB (any bigger files will be ignored), however this limit can be increased in in the Miscellaneous parameter.

This bridge detects the following standard Parquet data types:
as defined in https://parquet.apache.org/documentation/latest

BOOLEAN: 1 bit boolean
INT32: 32 bit signed ints
INT64: 64 bit signed ints
INT96: 96 bit signed ints
FLOAT: IEEE 32-bit floating point values
DOUBLE: IEEE 64-bit floating point values
BYTE_ARRAY: arbitrarily long byte arrays.

APACHE ORC FILES
This bridge imports metadata from ORC files using a Java API.
Note that this bridge is not performing any data driven metadata discovery, but instead reading the schema definition at the header (top) of the ORC file.

This bridge detects the following standard ORC data type:
as defined in hhttps://orc.apache.org/docs/types.html

Integer: boolean (1 bit), tinyint (8 bit), smallint (16 bit), int (32 bit), bigint (64 bit)
Floating point: float, double
String types: string, char, varchar
Binary blobs: binary
Date/time: timestamp, timestamp with local time zone, date
Compound types: struct, list, map, union

MORE INFORMATION
Please refer to the individual parameter's tool tips for more detailed examples.


Bridge Parameters

Parameter Name Description Type Values Default Scope
Driver version Choose driver version according to Kafka API.
Used to load the necessary version-specific libraries.
ENUMERATED
2.2.0
2.1.1
2.1.0
2.0.1
2.0.0
1.1.1
1.1.0
1.1.0  
Bootstrap servers List of 'host:port' pairs to use for establishing the initial connection to the Kafka cluster, and finding available servers and topics, e.g.
'host1:port1, host2:port2'

The list does not need to include all available servers but should have at least one.
You may want to include more than one server in case any of them are down.
The first entry from this list would be user as a cluster name.
STRING   localhost:9092 Mandatory
Schema Registry URL Comma-separated list of URLs for Schema Registry instances that can be used to look up schemas.
See https://docs.confluent.io/current/schema-registry/connect.html#configuration-options for details.
STRING   http://localhost:8081  
Topics List of topic names, such as 'topic1, topic2'.
If list is empty, then all topics are available.
You can specify topic names as an wildcard pattern:
'topic?'

'*topic*'

'topic_?,*topic*'
REPOSITORY_SUBSET      
Number of sample messages The maximum number of messages to sample from topics. These messages are used to identify topic format details, like field names and data types. STRING   1000  
Use SSL protocol to connect Set this parameter to True when the Kafka consumer uses TLS/SSL to encrypt Kafka's network traffic.

Kafka uses SSL to encrypt connections between the server and clients
BOOLEAN
False
True
False  
Truststore file The location of the trust store file.
If it is empty the bridge would try to locate it in 'java.home'\lib\security\{'jssecacerts'|'cacerts'}
FILE *.*    
Password of the truststore Password of the truststore. PASSWORD      
Keystore file The location of the keystore file. FILE *.*    
Password of the keystore Password of the keystore. PASSWORD      
Password of the key Password of the key. PASSWORD      
JAAS configuration path Enter the primary part of the Kerberos principal you defined for the brokers when you were creating the broker cluster. For example, in this principal kafka/kafka1.hostname.com@EXAMPLE.COM, the primary part to be used to fill in this field is kafka.

Kafka property value -
sasl.kerberos.service.name=value
FILE *.*    
Kafka brokers principal name Enter the primary part of the Kerberos principal you defined for the brokers when you were creating the broker cluster. For example, in this principal kafka/kafka1.hostname.com@EXAMPLE.COM, the primary part to be used to fill in this field is kafka.
This value is going to Kafka property: sasl.kerberos.service.name=value
STRING      
Kinit command path Kerberos uses a default path to its Kinit executable. If you have changed this path, select this check box and enter the custom access path.

Kafka property value -
sasl.kerberos.kinit.cmd=value
STRING      
Kerberos configuration path Kerberos uses a default path to its configuration file, the krb5.conf file (or krb5.ini in Windows) for Kerberos 5 for example. If you leave this parameter clear, a given strategy is applied by Kerberos to attempt to find the configuration information it requires.
For details about this strategy, see the Locating the krb5.conf Configuration file section in Kerberos requirements.

This value is going to JVM -
'-Djava.security.krb5.conf=value'
FILE *.*    
Miscellaneous INTRODUCTION
Specify miscellaneous options starting with a dash and optionally followed by parameters, e.g.
-connection.cast MyDatabase1="SQL Server"
Some options can be used multiple times if applicable, e.g.
-connection.rename NewConnection1=OldConnection1 -connection.rename NewConnection2=OldConnection2;
As the list of options can become a long string, it is possible to load it from a file which must be located in ${MODEL_BRIDGE_HOME}\data\MIMB\parameters and have the extension .txt. In such case, all options must be defined within that file as the only value of this parameter, e.g.
ETL/Miscellaneous.txt

JAVA ENVIRONMENT OPTIONS
-java.memory <Java Memory's maximum size> (previously -m)

1G by default on 64bits JRE or as set in conf/conf.properties, e.g.
-java.memory 8G
-java.memory 8000M

-java.parameters <Java Runtime Environment command line options> (previously -j)

This option must be the last one in the Miscellaneous parameter as all the text after -java.parameters is passed "as is" to the JRE, e.g.
-java.parameters -Dname=value -Xms1G
The following option must be set when a proxy is used to access internet (this is critical to access https://repo.maven.apache.org/maven2/ (and exceptionally a few other tool sites) in order to download the necessary third party software libraries.
-java.parameters -Dhttp.proxyHost=127.0.0.1 -Dhttp.proxyPort=3128 -Dhttps.proxyHost=127.0.0.1 -Dhttps.proxyPort=3128 -Dhttp.proxyUser=user -Dhttp.proxyPassword=pass -Dhttps.proxyUser=user -Dhttps.proxyPassword=pass

-java.executable <Java Runtime Environment full path name> (previously -jre)

It can be an absolute path to javaw.exe on Windows or a link/script path on Linux, e.g.
-java.executable "c:\Program Files\Java\jre1.8.0_211\bin\javaw.exe"

-environment.variable <name>=<value> (previously -v)

None by default, e.g.
-environment.variable var2="value2 with spaces"

MODEL IMPORT OPTIONS
-model.name <model name>

Override the model name, e.g.
-model.name "My Model Name"

-prescript <script name>

The script must be located in the bin directory, and have .bat or .sh extension.
The script path must not include any parent directory symbol (..).
The script should return exit code 0 to indicate success, or another value to indicate failure.
For example:
-prescript "script.bat arg1 arg2"

-cache.clear

Clears the cache before the import, and therefore will run a full import without incremental harvesting.
Warning: this is a system option managed by the application calling the bridge and should not be set by users.

-backup <directory>

Full path of an empty directory to save the metadata input files for further troubleshooting.

DATA CONNECTION OPTIONS
Data Connections are produced by the import bridges typically from ETL/DI and BI tools to refer to the source and target data stores they use. These data connections are then used by metadata management tools to connect them (metadata stitching) to their actual data stores (e.g. databases, file system, etc.) in order to produce the full end to end data flow lineage and impact analysis. The name of each data connection is unique by import model. The data connection names used within DI/BI design tools are used when possible, otherwise connection names are generated to be short but meaningful such as the database / schema name, the file system path, or Uniform Resource Identifier (URI). The following options allows to manipulate connections. These options replaces the legacy options -c, -cd, and -cs.

-connection.cast ConnectionName=ConnectionType

Casts a generic database connection (e.g. ODBC/JDBC) to a precise database type (e.g. ORACLE) for SQL Parsing, e.g.
-connection.cast "My Database"="SQL SERVER".
The list of supported data store connection types includes:
ACCESS
CASSANDRA
DB2
DENODO
HIVE
MYSQL
NETEZZA
ORACLE
POSTGRESQL
PRESTO
REDSHIFT
SALESFORCE
SAP HANA
SNOWFLAKE
SQL SERVER
SYBASE
TERADATA
VECTORWISE
VERTICA

-connection.rename OldConnection=NewConnection

Renames an existing connection to a new name, e.g.
-connection.rename OldConnectionName=NewConnectionName
Multiple existing database connections can be renamed and merged into one new database connection, e.g.
-connection.rename MySchema1=MyDatabase -connection.rename MySchema2=MyDatabase

-connection.split oldConnection.Schema1=newConnection

Splits a database connection into one or multiple database connections.
A single database connection can be split into one connection per schema, e.g.
-connection.split MyDatabase
All database connections can be split into one connection per schema, e.g.
-connection.split *
A database connection can be explicitly split creating a new database connection by appending a schema name to a database, e.g.
-connection.split MyDatabase.schema1=MySchema1

-connection.map DestinationPath=SourcePath

Maps a source path to destination path. This is useful for file system connections when different paths points to the same object (directory or file).
On Hadoop, a process can write into a CSV file specified with the HDFS full path, but another process reads from a HIVE table implemented (external) by the same file specified using a relative path with default file name and extension, e.g.
-connection.map hdfs://host:8020/users/user1/folder/file.csv=/user1/folder
On Linux, a given directory (or file) like /data can be referred to by multiple symbolic links like /users/john and /users/paul, e.g.
-connection.map /users/John=/data -connection.map /users/paul=/data
On Windows, a given directory like C:\data can be referred to by multiple network drives like M: and N:, e.g.
-connection.map M:\=C:\data -connection.map N:\=C:\data

-connection.casesensitive ConnectionName

Overrides the default case insensitive matching rules for the object identifiers inside the specified connection, provided the detected type of the data store by itself supports this configuration (e.g. Microsoft SQL Server, MySql etc.), e.g.
-connection.casesensitive "My Database"

KAFKA API OPTIONS
-consumer.group

A string that uniquely identifies the group of consumer processes to which this consumer belongs. By setting the same group ID, multiple processes indicate that they are all part of the same consumer group.
This value would be passed into Kafka 'group.ID' property.
STRING      

 

Bridge Mapping

Mapping information is not available

Last updated on Mon, 18 Oct 2021 17:37:48

Copyright © Meta Integration Technology, Inc. 1997-2021 All Rights Reserved.

Meta Integration® is a registered trademark of Meta Integration Technology, Inc.
All other trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.