# merge2er

> This module is used to merge smaller files of the same structure into larger files to address the small files problem in Hadoop and Spark.

---

Site [llms.txt](/flexter/v2.1/llms.txt)

---

## Parameters

To get all the supported merge2er parameters the following command can be used:

```bash
merge2er --help
```

All the compatible parameters that can be used are listed:

```text
Usage: merge2er [Options] [INPUT]

  INPUT                           Input location path

  Options:

  -h, --help

  --version

  # schema

  -j, --job ID                    Job ID

  # spark-submit

  --master MASTER_URL             spark://host:port, mesos://host:port, yarn, or local.

  --name NAME                     The name of your application.

  --jars JARS                     Comma-separated list of local jars to include on the driver

  --conf PROP=VALUE               Arbitrary Spark configuration property.

  -b, --blocksize BLOCKSIZE       Block Size, eg. 1024kb, 64mb, 128mb...

  -D, --warehouse-dir DIRECTORY   Spark SQL warehouse directory

  --metrics DURATION              Enabling metrics printing in defined interval: 1h, 1m, 1s...

  # input data

  -U, --in-user USER              Input user

  -P, --in-password PASSWORD      Input password

  --in-token TOKEN                Session token of input S3 bucket

  -F, --in-format FORMAT          Input format:
                                  - jdbc, parquet, orc, json, csv, tsv, psv, avro...

  --in-schema SCHEMA              Input schema related to hive or jdbc source

  --schema-pattern PATTERN        Input schema pattern related to hive or jdbc source: Ex "*word*"

  --table-pattern PATTERN         Input table pattern related to hive or jdbc source: Ex "*word*"

  -T, --table TABLE|QUERY         Name of the table (or specified SQL)

  -I, --id-column <NAME|EXPR>[:DATATYPE] [EXTRA] [AS ALIAS]
                                  Name of identifier column to contain document's ID values for tabular
                                  files, jdbc or hive tables

  -<MIN>, --id-min MIN            ID value specifying lower bound data range to be processed

  +<MAX>, --id-max MAX            ID value specifying upper bound data range to be processed

  --where WHERE                   Where clause for filtering tabular files, jdbc or hive tables

  --limit LIMIT                   Limit clause for filtering the number of records

  --has-header                    If the input csv/psv/tsv file has a header
                                  default: true

  --in-opt PROP=VALUE,...         Extra options for Spark dataFrame reader, ex: prop1=value1,prop2=value2

  # metadata

  -m, --meta URL                  JDBC location of metadata store
                                  default: jdbc:postgresql:x2er

  -M, --meta-user USER            Metadata user
                                  default: flex2er

  -w, --meta-password PASSWORD    Metadata password

  # output data

  -o, --out OUTPUT                Output location path

  -u, --out-user USER             Output user

  -p, --out-password PASSWORD     Output password

  --out-token TOKEN               Session token of output S3 bucket

  -B, --batchsize BATCHSIZE       Batch size to write into databases
                                  default: 1000

  -f, --out-format FORMAT         Output format:
                                  - jdbc, parquet, orc, json, csv, tsv, psv, avro...

  -z, --compression COMPRESSION   Compression mode for filesystem output
                                  - none, snappy, gzip, bzip2, deflate, lzo, lz4, zlib, xz...
                                  default: snappy

  -S, --savemode SAVEMODE         Save Mode when target table, directory or file exists
                                  ex: [e]rror, [a]ppend, [o]verwrite, [i]gnore, [p]rintschema, [w]riteschema
                                  default: append

  -Y, --out-part PARTITIONS       Number of partitions for writing data

  -V, --hive-create               Enable creating hive tables

  -E, --out-schema SCHEMA         Creating hive or jdbc tables into schema

  -r, --out-prefix PREFIX         Append a prefix in each output table name

  -y, --out-suffix SUFFIX         Append a suffix in each output table name

  --constraints                   Enables copying the primary and foreign key constraints

  -R, --extra-column <NAME|EXPR>[:DATATYPE] [AUTO|RANDOM|NOW|FILENAME|FILEPATH|=VALUE] [AS ALIAS]
                                  Extra column(s) to be included in all output tables

  -i, --partition-column <NAME|EXPR>[:DATATYPE] [AUTO|RANDOM|NOW|FILENAME|FILEPATH|=VALUE] [AS ALIAS]
                                  Partition column(s) to be included in all output tables

  -z, --compression COMPRESSION   File formats compression mode
                                  - none, snappy, gzip, bzip2, deflate, lzo, lz4, zlib, xz...
                                  default: snappy

  --rename [IDX=|NAME=]NEW_NAME,* Rename a table or multiple tables by index (1,2,3...) or by its original name

  --namemode MODE                 Change the table/column original names to:
                                      - "": Keep original names
                                    - [l]ower: to lower case.
                                    - [u]pper: to upper case.
                                    - [c]camelcase: Remove separators and keep words separated by case.

  --name-max-len SIZE             Maximum column name length
                                  default: 30

  --out-opt PROP=VALUE,...        Extra options for Spark dataFrame writer, ex: prop1=value1,prop2=value2

  # actions

  -c, --commands                  Show SQL commands

  -s, --skip                      Skip writing results
```