Quick tutorial demonstrating how to use uniVocity to load data from a file into a database.


Import world cities using uniVocity

This simple project was created to demonstrate how uniVocity can be used to load/transform massive amounts of data, and how it performs.

We will be using a couple of files made available for free by Maxmind. One is a 3 million plus rows CSV file, which has information about all the world's cities.

The size of this file can present some problems, especially if you need to load the data into a different database schema. Additionally, there are inconsistencies, such as incomplete data and duplicate cities, which we will try to address.

We created two examples for you:

What to expect

These ETL processes will perform differently depending on your setup and hardware. For reference, here's my (very) modest hardware, an ultrabook:

  • CPU: Intel i5-3337U @ 1.8 GHz
  • RAM: 4 GB
  • Storage: 128GB SSD drive
  • OS: Arch Linux 64 bit
  • DB: MySQL

These are the statistics I got after processing 3,173,958 rows:

Process Time to complete Rows inserted Memory Throughput License
MigrateWorldCities.java 61.835 seconds 3,032,002 3 GB 51,269.3 rows/s Evaluation
MigrateWorldCities.java 11.717 minutes 3,032,002 3 GB 4,509.3 rows/s Free
LoadWorldCities.java 64.094 seconds 3,173,958 300 MB 49,520.4 rows/s Evaluation
LoadWorldCities.java 8.069 minutes 3,173,958 300 MB 6,555.5 rows/s. Free

Notice that uniVocity is free for non-commercial use, but in this case batch operations are disabled. Nevertheless, the performance is not too bad considering the amount of data we are inserting without batching. We are constantly working to improve uniVocity's performance even further.

If you want to execute these processes with an evaluation license, run the RequestLicense.java to obtain a free 30-day trial license.

Setting up

Before executing the example processes, you need to download the input files and setup your database.

Download instructions.

Download the following files from Maxmind:

Unzip and place them inside the src/main/resources/files directory. You should end up with the following structure:


Database setup.

We created scripts to generate the required tables for you in the database of your preference. This project includes scripts for HSQLDB, MySQL, Oracle XE, Microsoft SQL Server and Postgres. You can use any other database if you want, just follow these instructions (feel free to submit a pull request with additional scripts your database).

Simply edit the connection.properties file with the connection details for your database of choice.

If you are using Oracle XE:

Please download the JDBC drivers (odjbc6.jar) from Oracle's website and add it to your classpath manually.

To ensure almost everyone is able to execute this project, we made it compatible with the JDK 6. If you are still using the JDK 6, ensure you are downloading the compatible version of the JDBC driver.

You may also want to configure the database itself to allow bigger batch sizes, and increase the number of open cursors.

If you are using Microsoft SQL Server:

Please download the JDBC drivers (sqljdbc4.jar) from Microsft's website and add it to your classpath manually.

Additionally, add the sqljdbc_auth.dll file that comes with the JDBC driver package to the project root.

Executing the processes

Just execute LoadWorldCities.java or MigrateWorldCities.java as a java program. The process will try to connect to your database and create the required tables if they are not present, and then ETL process will start.

If this is the first time you execute uniVocity, a pop-up will will be displayed asking if you agree with the uniVocity free license terms and conditions. Once you agree it will disappear and the process will start normally. Keep in mind that with the free license, batching is disabled.

Explaining the configurations in EtlProcess

The EtlProcess class is an abstract class that initializes a DataIntegrationEngine object and delegates the definition of data mappings between the CSV files and the database tables to its subclasses.

A brief introduction

uniVocity works with abstractions called data stores and data entities. A data entity is an abstraction over anything that is able to store and/or retrieve data, and a data store is used to access and manage of data entities. This allows us to use virtually anything as sources and destinations of data. In this example we will use uniVociy to simply extract data from one or more source data entities (the world cities files) and map the information to destination data entities (a few database tables).

In general, the first thing you need to do to use uniVocity is to configure a few data stores.

Configuring the CSV data store.

uniVocity comes with a few pre-defined data stores and you just need to provide some essential configurations. In the EtlProcess class, we create a CsvDataStoreConfiguration:

CsvDataStoreConfiguration csv = new CsvDataStoreConfiguration("csv");
csv.addEntities("files", "ISO-8859-1");

CsvEntityConfiguration regionCodesConfig = csv.getEntityConfiguration("region_codes");
regionCodesConfig.setHeaders("country", "region_code", "region_name");

In this snippet, new CsvDataStoreConfiguration("csv") creates a CSV data store with name "csv". setLimitOfRowsLoadedInMemory(batchSize) limits the number of rows loaded in memory by uniVocity at any given time. uniVocity will wait if the rows loaded during a data mapping cycle are taking too long to be consumed.

csv.addEntities("files", "ISO-8859-1") adds all files under the src/main/resources/files to this data store, and will read them with using the ISO-8859-1 encoding.

As the worldcitiespop.txt has a header row to identify each column, no configuration is required. uniVocity will detect the available fields automatically.

However, the region_codes.csv does not have a header row so we need to provide this information manually. csv.getEntityConfiguration("region_codes"); will return a configuration object for the region_code entity, which is used to provide the headers of the file. We also disable header extraction by invoking setHeaderExtractionEnabled(false). This way uniVocity won't consider the first row in the input file as the header row.

Configuring the JDBC data store.

When interacting with a database, you are likely to use uniVocity's built-in JdbcDataStoreConfiguration:

DataSource dataSource = database.getDataSource();
JdbcDataStoreConfiguration config = new JdbcDataStoreConfiguration("database", dataSource);

JdbcEntityConfiguration defaultConfig = config.getDefaultEntityConfiguration();


To create a JDBC data store, a javax.sql.DataSource is required. In this example we use the one initialized by the Database class using the connection details provided in the connection.properties file.

With a datasource ready, a JDBC data store configuration can be created with new JdbcDataStoreConfiguration("database", dataSource);. The name of this data store will be "database".

Using the getDefaultEntityConfiguration() method, we can define default configurations for all entities (in this case tables) in the "database" data store. setBatchSize(batchSize) defines the size of bulk insert/update/delete operations over all tables in this data store.

retrieveGeneratedKeysUsingStatement(true) configures how generated keys should be extracted upon insertion of new rows into any table of this database. In this case generated keys will be retrieved from the java.sql.Statement used to insert data. The true flag indicates that the insert operations can be batched and the JDBC driver supports returning all generated keys after a batch operation. Some JDBC drivers do not support this and in this case batch insertion must be disabled. To circumvent this limitation, we implemented some strategies to allow insertions in batch with retrieval of generated keys in the JdbcEntityConfiguration class.

Finally, any other database-specific configuration is applied by the underlying implementation of Database. Refer to the implementation of each specific database here to learn more.

Our JDBC data store implementation will identify all available tables in your database and initialize them as JDBC data entities. So that's all you need to do in terms of configuration.

Configure a data integration engine

With the data stores properly configured, you can create an EngineConfiguration object:

EngineConfiguration config = new EngineConfiguration(engineName, databaseConfig, fileConfig);

With the engine configuration ready, you can just register this engine to make it available anywhere in your application:


Once the engine has been registered, you can get an instance of DataIntegrationEngine using:

engine = Univocity.getEngine(engineName);

You are now ready to define/modify data mappings among entities of your data stores and execute data mapping cycles!

Configuring how metadata is stored (optional)

To perform operations such as data change autodetection (not demonstrated here) and reference mappings (used in MigrateWorldCities.java), uniVocity generates metadata for each record persisted. You can define where this metadata should be stored or simply use uniVocity with its in-memory metadata database, which is created automatically. If you want to use your database to store metadata, ensure it is tuned to allow fast insert operations.

To configure the metadata storage, create a MetadataSettings object with the javax.sql.DataSource that provides connections to your database.

MetadataSettings metadata = new MetadataSettings(metadataDatabase.getDataSource());

Finally, simply invoke the engineConfiguration.setMetadataSettings(metadataConfig) method to configure your data integration engine to use this database.

Loading data with LoadWorldCities

The LoadWorldCities class is quite straightforward as a few simple field mappings are required:

DataStoreMapping mapping = engine.map("csv", "database");
EntityMapping cityMapping = mapping.map("worldcitiespop", "worldcities");
cityMapping.identity().associate("country", "city", "region").to("country", "city_ascii", "region");

Using the DataIntegrationEngine initialized by the parent class EtlProcess, we create mapping between the 2 datastores configured previously: engine.map("csv", "database") will create a DataStoreMapping, which will be used to define mappings between their data entities.

map("worldcitiespop", "worldcities") will create an EntityMapping object which will be used to associate fields of the worldcitiespop.txt file with columns of the WorldCities database table.

All we have to do now is to associate fields in the source with the fields in the destination. uniVocity requires you to elect a combination of one or more fields as the identifiers of each record of both entities. This is done with the command identity().associate("country", "city", "region").to("country", "city_ascii", "region"). Unless other entity mappings have references to the values mapped here, the uniqueness of the identifier values is not essential. The list of fields inside the associate method selects the columns in the worldcitiespop.txt file, while the list of fields inside the to method selects the fields of the WorldCities table. Altough unusal, the identity of a mapping does not need to involve the primary keys of a database table.

value().copy("accentCity").to("city") creates a value mapping, which will simply copy the values in the accentCity column of our CSV file to the column city of our database table.

As the fields population, latitude and longitude have the same names on both source and destination entities, we can simply call autodetectMappings().

The last line in the snippet defines how the data should be persisted in the destination: persistence().notUsingMetadata().deleteAll().insertNewRows() configures the entity mapping to not generate any metadata (as we won't use it for anything useful in this example), to delete all rows in the WorldCities table and to insert the new rows mapped from the source entity.

Finally, we can execute a mapping cycle. This is done in the main method of the LoadWorldCities class

LoadWorldCities loadCities = new LoadWorldCities();
try {
} finally {

Migrating data with MigrateWorldCities.java

The MigrateWorldCities.java is a bit more involved than the LoadWorldCities class as we are mapping the information to a new schema, eliminating duplicate/invalid records and processing foreign keys.

We start by adding a function to the engine to convert some String values to upppercase, as the character case of region and country codes is not consistent in our input files:

engine.addFunction(EngineScope.STATELESS, "toUpperCase", new FunctionCall<String, String>() {
    public String execute(String input) {
        return input == null ? null : input.toUpperCase();

You can use functions to perform all sorts of powerful operations, refer to the FunctionCall javadoc for more information.

Getting back to our mappings, we start by creating a mapping from the CSV entity region_codes.csv to the database table region.

EntityMapping regionMapping = mapping.map("region_codes", "region");
regionMapping.identity().associate("country", "region_code").toGeneratedId("id").readingWith("toUpperCase");
regionMapping.value().copy("country", "region_name").to("country", "name");

The identity mapping will associate the combination of values (converted to uppercase) from country and region_code in the source CSV with the generated primary key id of the region table.

With value().copy("country", "region_name").to("country", "name") we will copy the country and region_name from the CSV to the table columns country and name respectively.

Finally, the persistence().usingMetadata().deleteAll().insertNewRows(); instructs uniVocity to generate metadata for this entity mapping. This means that it will create some information for each mapped record that enables features such as identification of modified data in the source entity, and reference mappings. In this example we are interested in the reference mappings only, as can be seen in the next entity mapping:

EntityMapping cityMapping = mapping.map("worldcitiespop", "city");
cityMapping.identity().associate("country", "city", "region").toGeneratedId("id");

cityMapping.reference().using("country", "region").referTo("region_codes", "region").on("region_id").readingWith("toUpperCase").onMismatch().discard();


Here we map all cities in the worldcitiespop file into the database table city.

The reference mapping reference().using("country", "region").referTo("region_codes", "region").on("region_id").readingWith("toUpperCase") states that: with the values from fields country and region in the worldcitiespop CSV (converted to uppercase), query the metadata of the entity mapping from region_codes to region, to find the identifiers generated for the combination of country and region codes, and copy that identifier to the region_id column of the database table city. This mapping will generate correct foreign keys, and uniVocity ensures the data is consistent.

Still on the reference mapping, onMismatch().discard() instructs uniVocity to discard any rows from the worldcitiespop CSV that do not have a correct association to a region (the file contains regions with null/inexistent region codes);

Again, we use autodetectMappings() to automatically generate mappings to copy values from fields whose names are the same in both entity and destination.

With persistence().notUsingMetadata().deleteAll().insertNewRows() we indicate that no metadata is to be generated for this mapping. We just want to delete all rows in the city table and insert the data mapped from worldcitiespop.

The last step in this mapping definition is to attach a RowReader to the input of this mapping. It will collect the values from worldcitiespop fields country, city and region, and discard any input row with duplicate or missing data:

public void initialize(RowMappingContext context) {
    //before processing the first row, we have a chance to get the indexes of each
    //input field we are interesed in
    COUNTRY = context.getInputIndex("country");
    CITY = context.getInputIndex("city");
    REGION = context.getInputIndex("region");

public void processRow(Object[] inputRow, Object[] outputRow, RowMappingContext context) {
    //let's create an entry with the input data
    String entry = createEntry(inputRow);

    //if the entry is not null, we check whether it has been processed already
    if (entry != null) {
        if (entries.contains(entry)) {
            //if it has, then we discard the row
        } else {
            //if it hasn't then we store the entry so any other duplicate entry will be discarded
    } else {
        //if the information in the row is incomplete, we discard the row.

private String createEntry(Object[] inputRow) {
    //Let's get the values of the elements we are interested in
    String country = (String) inputRow[COUNTRY];
    String city = (String) inputRow[CITY];
    String region = (String) inputRow[REGION];

    //If any of this information is null, then the row will be discarded
    if (country == null || city == null || region == null) {
        return null;


    return entryBuilder.toString().toLowerCase();

Finally, we can execute a mapping cycle from the main method of MigrateWorldCities.java Remember to set the JVM args to execute with 3 gigabytes of memory (i.e. -Xms3G -Xmx3G), as the RowReader stores more than 3 million entries into a hashset to identify and eliminate duplicates.

MigrateWorldCities migrateCities = new MigrateWorldCities();
try {
} finally {

And that's it

We hope you enjoyed this tutorial. It probably took more time to explain it than to actually write the code. We work very hard to make uniVocity your framework of choice to implement all sorts of ETL tools, data mapping, data integration and data synchronizaion solutions.

If you have any questions and suggestions don't hesitate to e-mail us