Databaseliner is a tool for spidering a database to produce a referentially integral subset of data.
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DataBaseliner is a tool that allows a self contained subset of data to be extracted from a database. By defining key seed data to extract and setting up relationships dataBaseliner will extract a slice of data from a source database that is completely referentially integral and output this as a SQL script that can be run into development and test schemas.

DataBaseliner is written using Java and jdbc to connect to databases, so that it can work with any database that provides a compliant jdbc driver. As different drivers and databases have different idiosyncrasies and requirements for SQL generation a flexible extension mechanism is used to add support for different databases. Support for each database is provided as a separate jar / sub module.

Configuring DataBaseliner

DataBaseliner is configured using an xml document that defines how to connect to the database, what to extract, and how to output it. The document is made up of:

  • databaseConnection - configures the source database
  • extractionPlan - configures the data to extract
  • extractions - configure how to seed the extraction
  • relationships - configure how to spider across the database from the seed tables
  • manipulations - configure how to modify the extracted data before outputting
  • outputDetails - configures the output of the extraction

skeleton xml:

    <extractionPlan dryRun="false">...</extractionPlan>


This section configures the jdbc connection details of the source database and the dialect for dataBaseliner to use. For example:

    <user>db user</user>

The currently available dialects are:

  • org.databaseliner.dialect.postgres.PostgresDialect

More information about adding dialects can be found on the wiki (shortly).


This section configures the output from dataBaseliner:


setting preserveDatabaseIntegrity to true will make the resulting sql script produce insert and update statements in the order required to be run into a database with all constraints enabled. Note this makes outputting the script slower and is likely to take longer to insert into your target database.


This is the meat of the dataBaseliner configuration. The extraction plan describes the source database and the data you want to extract. The plan is made up of ignore, extraction and relationship nodes:

<extractionPlan dryRun="false">
    <ignore ... />
    <ignore ... />

    <extraction .../>
    <extraction .../>

    <relationship .../>
    <relationship .../>

Setting dryRun to true does not extract any data but instead explores the database metadata to check the configured extraction plan. This produces an html report which you can use to check your plan is correct and will pull back data from the expected tables.


<ignore schema="SCHEMA" table="TABLE" />

Does what it says on the tin. No data will be automatically extracted from tables marked as ignored.

If you configure an extraction or an explicit relationship with the ignored table data will be extracted for it, this allows you to control precisely what data gets put in the table.


Extractions configure the seed data set for your exrtraction. There are 3 types of extractions:

  • all - This extraction pulls in all data in a table, this is commonly used for static data tables.
  • matchingIds - This extraction pulls in all data in a table where the value in a given column matches one of the provided ids. This allows you to extract known data from the database, for example pulling in known clients from a accounting database.
  • query - Seed extraction that will extract rows from a table using an arbitrary SQL query. This is intended to be used when the data you want can not be simply defined by one table and a finite set of ids. This includes when the seed data set is defined by joining multiple tables or when you want to extract data based on more complex 'where' logic as opposed to a known data set (for example extracting all the data for the last 7 days).

More information about the extractions, including example configuration xml can be found on the wiki and in the javadocs for the extraction classes.


Relationships configure how databaseliner spiders out from the seed extraction tables to produce a consistent dataset. By default databaseliner will follow all foreign keys defined in you database so that if you have data in table A that refers to data in table B the rows in table B that are referenced will be extracted. Databaseliner will continue following foreign keys until a fully referentially consistent data set is extracted.

Following foreign keys will however only get you do far. In an reasonably complex database schema you will have relationships between tables where the direction of the foreign keys means that data will not automatically be extracted. You may also have join tables where you only want to pull in data that matches data in multiple tables, or even relationships you only want to extract if certain criteria are met.

Additionally if your database does not have any foreign keys configured you can define the relationships between tables manually using the relationship xml.

To configure how databaseliner spiders data in these scenarios you can add relationships in the extraction plan. The available relationship types are:

  • ignored - Tells databaseliner to explicitly ignore a foreign key relationshiop that would otherwise be followed. This allows you to configure another relationship to extract the data or simply ignore the relationship.
  • refersTo - Relationship from a single table to another table. This acts like the default foreign key extraction that gets data from a table that is referred to by the seed table except that no foreign need exist in the database. This can be used to model the inverse of an existing foreign key, extracting all the data in a table that refers to the seed table, or where no foreign key relationship is defined (for example where audit data about an object outlives the object itself so no foreign key can be defined).
  • compositeReferingToMultipleTables - Relationship where the data in the target table is defined by 2 or more tables. This allows data to be restricted so that it is only extracted when it refers to data both in the defined tables. By using this relationship you can manage the join tables between different areas of your database extraction without them blowing up and pulling in undesired data. For example in an audit system that logs different events against objects this extraction can retrieve all audit records for extracted objects with the extracted audit types. Without using this relationship type extracting data for the join could retrieve every audit of the desired type and cause every object in the system to be extracted. This could then in turn pull in every audit type in the system resulting in the entire database being extracted.
  • conditionalOnSeedTable - Relates a single table to another table but only populates data when the data in seed table matches the provided condition. This is used when a table is used to provide a layer of indirection to another table or tables. By adding conditionalOnSeedTable relationships with conditions you can define how to follow the indirection and extract desired data.


The manipulations section of the databaseliner configuration allows you to specify any changes to the extracted data prior to outputting the sql file. Manipulations are useful when the target database schema does not match the source schema, when you need to change the values in certain columns or streamline the output sql.

The available manipulation types are:

  • renameTable - This manipulation is used when a table in the target schema has a different name to table extracted from. This situation arises when data is extracted from more than one schema and inserted into a single consolidated schema. In this case the tables in different schemas can have the same name and the table in one schema be referenced by a synonym. Using this manipulation the table can be renamed to have the same name as the synonym and be used transparently in the target schema.
  • removeColumn - This is used if the column is not present in the target schema or if you want to leave it blank (or let the database default the values for you)
  • addColumn - This is used if you need to add a new column to a table, this can occur if the target schema is different from the source schema (this can occur if work development work has been done on the target schema which has not been applied to the source schema yet)
  • renameColumn - This is used if you need to add a new column to a table, this can occur if the target schema is different from the source schema (this can occur if work development work has been done on the target schema which has not been applied to the source schema yet)
  • updateField - This is used when data in the source schema is not appropriate for use in the target schema. For example you might be extracting from a live database with real user's data in it, the update field manipulator can be used to change their passwords to a known value in the target schema (so you can log in as them) and to obfuscate their credit card details. Fields can be set to explicitly defined values, values from other columns on the table, arbitrary sql statements or output of a given FieldManipulator class.


Since CLOBs can't be inserted by a standard insert script databaseliner handles them slightly differently. Rather than put the text value of the CLOB in the insert statement the text is written out to the output directory. The text files are placed in a directory structure that represents the table and column that the CLOB is from and the filename reflects the primary key representing the CLOB's row. The insert script sets the value of the CLOB to "placeholder".

To load the correct values for the CLOBs there is an ant task (placeholderLoader) that scans the output directory structure and streams the values from the files into the correct rows in the database.

As writing CLOBS to disk and then streaming them into the target database is not the most efficient way of doing business there is a manipulation available in the oracle dialect (and potentially in future dialects) that can check the size of the text stored in the CLOB and if it is short enough to be inserted as a conventional string then it will output the string insert rather than write the CLOB out to disk. This can improve performance when a CLOB field is used to provide unlimitted text capacity but most values are reasonably small.

Note all CLOBs are written out as utf-8 encoded files so that they include all accents and exotic characters you have in your database.

More readme to come soon

in the meantime look at the code or generate the javadocs, the extractions, relationships and manipulations are described in detail.