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

MIMB Bridge Documentation

MIMB Import Bridge from Apache Hadoop Distributed File System (HDFS Java API)

Bridge Specifications

Vendor Apache
Tool Name Hadoop Distributed File System (HDFS)
Tool Version 2.5
Tool Web Site http://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html
Supported Methodology [File System] Multi-Model, Data Store (NoSQL / Hierarchical, Physical Data Model) via Java API

BRIDGE INFORMATION
Import tool: Apache Hadoop Distributed File System (HDFS) 2.5 (http://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html)
Import interface: [File System] Multi-Model, Data Store (NoSQL / Hierarchical, Physical Data Model) via Java API from Apache Hadoop Distributed File System (HDFS Java API)
Import bridge: 'ApacheHDFS' 11.0.0

BRIDGE DISCLAIMER
This bridge requires internet access to https://repo.maven.apache.org/maven2/ (and exceptionally a few other tool sites)
in order to download the necessary third party software libraries into $HOME/data/download/MIMB/
(such directory can be copied from another MIMB server with internet access).
By running this bridge, you hereby acknowledge responsibility for the license terms and any potential security vulnerabilities from these downloaded third party software libraries.

BRIDGE DOCUMENTATION
This bridge crawls a data lake implemented on the Apache (Cloudera, Hortonworks, etc.) Hadoop Distributed File System (HDFS) 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, Data,. String).

The bridge uses Apache Hadoop HDFS Java library (JARs) to access Hadoop file system.
The library JAR files are located in the /java/Hadoop directory.
One may specify a Configuration files directory and often that is sufficient, as the values for the other bridge parameters may be specified there.

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 filed 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-

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 filed 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, unionn

MORE INFORMATION

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


Bridge Parameters

Parameter Name Description Type Values Default Scope
Configuration files directory Directory containing core-site.xml and hdfs-site.xml for your environment.

It is an optional parameter that allows you to reuse configuration files you have and avoid specifying Hadoop connection and Kerberos security details manually using other parameters.

When you would like to specify the details manually you should leave this parameter value empty. If you specify the directory value and it does not have the configuration files the bridge exits with the error.

You can override the parameters available in the configuration files using the bridge parameters.
For example, you can override the fs.default.name file parameter using the NameNode URI bridge parameter.
DIRECTORY      
NameNode URI URI of the Hadoop NameNode, like hdfs://host::8020
To access the NameNode through the WebHDFS REST interface specify 'webhdfs' protocol, like like webhdfs://host::8020
STRING   [web]hdfs://[server host]:[port]  
Root directory Enter the directory containing metadata files or specify it using browsing tool. Bridge provides up to 3 level browsing depth. REPOSITORY_MODEL      
Include filter The include folder and file filter pattern relative to the root directory.
The pattern uses extended unix glob case-sensitive expression syntax.
Here are some common examples:
*.* - include any file at the root level
*.csv - include only csv files at the root level
**.csv -include only csv files at any level
*.{csv,gz} include only csv or gz files at the root level
dir\*.csv - include only csv files in the 'dir' folder
dir\**.csv - include only csv files under 'dir' folder at any level
dir\**.* - include any file under 'dir' folder at any level
f.csv - include only f.csv under root level
**\f.csv - include only f.csv at any level
**dir\** - include all files under any 'dir' folder at any level
**dir1\dir2\** - include all files under any 'dir2' folder under any 'dir1' folder at any level
STRING      
Exclude filter The exclude folder and file filter pattern relative to the root directory.
The pattern uses the same syntax as the Include filter. See it for the syntax details and examples.
Files that match the exclude filter are skipped.
When both include and exclude filters are empty all folders and files under the Root directory are included.
When the include filter is empty and the exclude one is not folders and files under the Root directory are included except ones matching the exclude filter.
STRING      
Partition directories Files-based partition directories' paths.
The bridge tries to detect partitions automatically. It can take a long time when partitions have a lot of files.
You can shortcut the detection process for some or all partitions by specifying them in this parameter.
Specify the partition directory path relative to the Root directory.
Use . to specify the root directory as the partitioned directory.
Separate multiple paths with the , (or ;) character.

ETL tools can read and write to pattern-based partitions directories.
For example, ETL can read all *.csv files from a folder F. The ETL bridge representes it as the '*.csv' dataset in the 'F' folder (F/*.csv).
You can instruct this bridge to generate the matching dataset by specifying its name in square brackets after the folder name, like F[*.csv].
Similar it true for application specific partitions.
For example, ETL can write files under folder F to partition sub-folders named using the 'getDate@[yyyyMMdd]' function expression.
The result is represented as the 'getDate@[yyyyMMdd]' dataset in the 'F' folder (F/getDate@[yyyyMMdd]).
Agan, you can instruct this bridge to generate the matching dataset by specifying something like F/[getDate@[yyyyMMdd]].

You may specify additional info about partitioned directory internal structure, using [dataset name] and {partitioned column name} patterns for following cases:
For application partitions like:
zone/po/us/2018/00001.csv
use: zone/[po]/{region}/{year}/*.csv or
zone/[po]/{*}/{*}/*.csv
if partition columns names are not important. They will be stitched by positions

For custom application partitions like:
zone/table1/2018/data/00001.csv
zone/table1/2018/log/00001.txt
zone/table2/2018/data/00001.csv
zone/table2/2018/log/00001.txt
use: zone/*/{year}/[data]/*.csv, zone/*/{year}/[log]/*.txt

For file based partitions like:
zone/mlcs.dataset1_data_document_20190219_132315.125.csv
zone/mlcs.dataset1_data_document_20190313_232416.225.csv
zone/mlcs.dataset1_data_document_20190414_532317.535.csv
zone/mlcs.dataset2_data_document_20190211_131215.125.xml
zone/mlcs.dataset2_data_document_20190314_130316.225.xml
zone/mlcs.dataset2_data_document_20190416_132317.535.xml

use: zone/mlcs.[dataset1]_data_document_{date}.csv,zone/mlcs.[dataset2]_data_document_{date}.xml
STRING      
Partition file number Number of files to scan during data-partitioning directories analyze. This parameter doesn't work when 'Partition directories' parameter is specified. NUMERIC      
Hadoop properties Custom Hadoop and HDFS configuration properties.

The bridge uses a default configuration to access a Hadoop distribution. If you need to use a custom configuration, specify its parameter values here.

For further information about the properties required by Hadoop and its related systems such as HDFS and Hive, see the documentation of the Hadoop distribution you are using or see Apache's Hadoop documentation on http://hadoop.apache.org/docs and then select the version of the documentation you want. For demonstration purposes, the links to some properties are listed below:
Typically, the HDFS-related properties can be found in the hdfs-default.xml file of your distribution, such as
http://hadoop.apache.org/docs/r2.6.0/hadoop-project-dist/hadoop-hdfs/hdfs-default.xml.
STRING      
Keytab file Full path to the Kerberos keytab file. The file is necessary to log into a Kerberos-enabled Hadoop system. It contains pairs of Kerberos principals and encrypted keys. You need to enter the Principal using the Principal user parameter.

The user that runs the bridge is not necessarily the one the Principal designates but must have the right to read the keytab file being used. For example, the user name you are using to run the bridge is UserA and the principal to be used is UserB; in this situation, ensure that UserA has the right to read the keytab file to be used.
STRING      
Principal User principal name. See the “Keytab file” parameter documentation for details. STRING      
Username User authentication name of HDFS. Sometimes referred to as proxy name.
The parameter is only used for Kerberos authentication.
It does not impact the user which runs the bridge.
STRING      
HDFS encryption key provider (KMS) The location of the KMS proxy. For example, kms://http@localhost:16000/kms.
Specify the HDFS encryption key provider only when the HDFS transparent encryption has been enabled in your cluster. Leave the value empty otherwise.
For further information about the HDFS transparent encryption and its KMS proxy, see Transparent Encryption in HDFS at https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/TransparentEncryption.html.
STRING      
Incremental import Specifies whether to import only the changes made in the source or to re-import everything (as specified in other parameters).

True - import only the changes made in the source.
False - import everything (as specified in other parameters).

An internal cache is maintained for each metadata source, which contains previously imported models. If this is the first import or if the internal cache has been deleted or corrupted, the bridge will behave as if this parameter is set to 'False'.
BOOLEAN
False
True
True  
Miscellaneous Specify miscellaneous options identified with a -letter and value.

For example, -m 4G -f 100 -j -Dname=value -Xms1G

-m the maximum Java memory size whole number (e.g. -m 4G or -m 2500M ).
-v set environment variable(s) (e.g. -v var1=value -v var2="value with spaces").
-j the last option that is followed by Java command line options (e.g. -j -Dname=value -Xms1G).
-hadoop key1=val1;key2=val2 to manualy set hadoop configuration options
-tps 10 maximum threads pool size
-tl 3600s processing time limit in s -seconds m - minutes or h hours;
-fl 1000 processing files count limit;
-delimited.top_rows_skip 1 number of rows to skip while processing csv files
-delimited.extra_separators ~,||,|~ comma separated extra delimiters each of which will be used while processing csv files
-delimited.no_header by default, bridge automatically tries to detect headers while processing csv files(basing on header columns types), use this option to disable headers import(f.e. to hide sensitive data)
-fresh.partition.models - use to import latest modified files when processing partitions defined in Partitioned directories parameter
-subst K: C:/test - use to associate a root path part with a drive or another path.
-skip.download - use to disable dependencies downloading and use only download cache
-prescript [cmd] - runs a script command before bridge execution. Example: -prescript \"script.bat\"
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.
-disable.partitions.autodetection - use this option to disable automatic partitions detection(when "Partition directories" option is empty)
-parquet.compressed.max.size=10000000 bridge will ignore parquet archives with size bigger then defined with this option value; default value is 10 000 000 Bytes;
STRING      

 

Bridge Mapping

Mapping information is not available

Last updated on Mon, 23 Mar 2020 17:37:49

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