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 Hadoop 2.x to 3.x
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

SPECIFICATIONS
Tool: Apache / Hadoop Distributed File System (HDFS) version Hadoop 2.x to 3.x via Java API
See http://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html
Metadata: [File System] Multi-Model, Data Store (NoSQL / Hierarchical, Physical Data Model)
Component: ApacheHDFS version 11.0.0

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/
- 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 access to internet, that directory can be copied from another 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.

OVERVIEW
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, Date, 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

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 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, 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
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, e.g.
webhdfs://host::8020
STRING   [web]hdfs://[server host]:[port]  
Root directory Enter the directory containing metadata files or specify it using browsing tool.
This bridge provides up to 5 levels of 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. The following paragraphs present several examples.

Application specific partitions
The ETL can write files under the folder F to partition sub-folders named using a function expression (e.g., 'getDate@[yyyyMMdd]'). The result is represented as the 'getDate@[yyyyMMdd]' dataset in the 'F' folder.
Generate the matching dataset by specifying:
F/[getDate@[yyyyMMdd]]

Partitioned directory internal structure
Specify additional info about the partitioned directory internal structure by using the [dataset name] and the {partitioned column name} patterns.
Example folder and file structure:
zone1/po/us/2018/00001.csv
Generate the matching dataset by specifying:
zone1/[po]/{region}/{year}/*.csv
or, if partition columns names are not important (They will be stitched by position):
zone1/[po]/{*}/{*}/*.csv

Partitioned directory internal structure custom application partitions
Example folder and file structure:
zone2/ACCT/10190103/bad/00001.csv
zone2/ACCT/10190103/good/00001.parquet
zone2/CUST/10190103/bad/00001.csv
zone2/CUST/10190103/good/00001.parquet
Generate the matching dataset by specifying:
zone2/*/{date}/[bad]/*.csv, zone2/*/{date}/[good]/*.parquet

File based partitions
Example folder and file structure:
zone3/mlcs.dataset1_data_document_20190219_132315.125.csv
zone3/mlcs.dataset1_data_document_20190313_232416.225.csv
zone3/mlcs.dataset1_data_document_20190414_532317.535.csv
zone3/mlcs.dataset2_data_document_20190211_131215.125.xml
zone2/mlcs.dataset2_data_document_20190314_130316.225.xml
zone3/mlcs.dataset2_data_document_20190416_132317.535.xml
Generate the matching dataset by specifying:
zone3/mlcs.[dataset1]_data_document_{date}.csv,zone3/mlcs.[dataset2]_data_document_{date}.xml
STRING      
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.

Username should be in the following format:
'<Name>@<Realm>'
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      
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"

FILE SYSTEM OPTIONS
-file.encoding <Encoding value>

Uses the encoding value to read the text files (e.g. delimited and fixed width).
The supported languages are listed below with the actual encoding value between parentheses at the end of each line, e.g.
-file.encoding shift_jis

Central and Eastern European (iso-8859-2)
Central and Eastern European (windows-1250)
Chinese Traditional (big5)
Chinese Simplified (GB18030)
Chinese Simplified (GB2312)
Cyrillic (iso-8859-5)
Cyrillic (windows-1251)
DOS (ibm-850)
Greek (iso-8859-7)
Greek (windows-1253)
Hebrew (iso-8859-8)
Hebrew (windows-1255)
Japanese (shift_jis)
Korean (ks_c_5601-1987)
Thai (TIS620)
Thai (windows-874)
Turkish (iso-8859-9)
Turkish (windows-1254)
UTF 8 (utf-8)
UTF 16 (utf-16)
Western European (iso-8859-1)
Western European (iso-8859-15)
Western European (windows-1252)
Locale encoding
No encoding conversion

-processing.max.threads <number> (previously -tps)

Allows for parallel processing up to a maximum number of threads (by default 1), e.g.
-processing.max.threads 10

-processing.max.time <time> (previously -tl)

Sets a time limit for processing all files. Time can be specified in seconds, minutes, or hours, e.g.
-processing.max.time 3600s
-processing.max.time 60m
-processing.max.time 1h

-processing.max.files <number> (previously -fl)

Sets a maximum number of files to process (there are no limits by default), e.g.
-processing.max.files 100

-partitions.refresh (previously -fresh.partition.models)

Uses the latest modified files when processing partitions defined in Partitioned directories parameter.

-partitions.disable.detection (previously -disable.partitions.autodetection)

Disables the automatic partition detection (when "Partition directories" option is empty)

-cache.reuse (previously -skip.download)

Reuses what was already downloaded in the cache by disabling dependencies downloading.

-hadoop.key <Hadoop configuration options> (previously -hadoop)

Sets key values for the hadoop libraries (None by default), e.g.
-hadoop.key key1=val1;key2=val2

-path.substitute <path> <new path> (previously -subst)

Substitutes a root path by a new one, e.g.
-path.substitute K: C:\test

DELIMITED FILE OPTIONS
-delimited.disable.header.parsing (previously -delimited.no_header)

Disables the parsing of the header of delimited files (headers are parsed by default to detect field names).
Use this option if the delimited file has no header, or to disable the import of the header (if the field names are sensitive).

-delimited.ignore.top.rows <number> (previously -delimited.top_rows_skip)

Skips an number of rows at the top of delimited files (by default 0).
Use this option the the delimited files contains several rows of description at the beginning.
-delimited.ignore.top.rows 1

-delimited.add.separators <comma separated separators> (previously -delimited.extra_separators)

Adds extra possible separators when parsing delimited files.
By default, the detected separators include: ', (comma)' , '; (semicolon)', ': (colon)', '\t (tab)', '| (pipe)', '0x1 (ctrl+A)', 'BS (\u0008)'
Note that extra separators can be multi characters, e.g.
-delimited.add.separators ~,||,|~

PARQUET FILE OPTIONS
-parquet.max.compressed.size <value> (previously -parquet.compressed.max.size)

Ignores any parquet archive files with a compressed size bigger than the provided value (Default value is 10 000 000 bytes), e.g.
-parquet.compressed.max.size=10000000
STRING      

 

Bridge Mapping

Mapping information is not available

Last updated on Wed, 18 Nov 2020 17:34:26

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