Getting Data into Your H2O Cluster

The first step toward building and scoring your models is getting your data into the H2O cluster/Java process that’s running on your local or remote machine. Whether you’re importing data, uploading data, or retrieving data from HDFS or S3, be sure that your data is compatible with H2O.

Supported File Formats

H2O currently supports the following file types:

  • CSV (delimited) files (including GZipped CSV)
  • ORC
  • SVMLight
  • ARFF
  • XLS (BIFF 8 only)
  • XLSX (BIFF 8 only)
  • Avro version 1.8.0 (without multifile parsing or column type modification)
  • Parquet

Notes:

  • ORC is available only if H2O is running as a Hadoop job.

  • Users can also import Hive files that are saved in ORC format (experimental).

  • If you encounter issues importing XLS or XLSX files, you may be using an unsupported version. In this case, re-save the file in BIFF 8 format. Also note that XLS and XLSX support will eventually be deprecated.

  • When doing a parallel data import into a cluster:

    • If the data is an unzipped csv file, H2O can do offset reads, so each node in your cluster can be directly reading its part of the csv file in parallel.
    • If the data is zipped, H2O will have to read the whole file and unzip it before doing the parallel read.

    So, if you have very large data files reading from HDFS, it is best to use unzipped csv. But if the data is further away than the LAN, then it is best to use zipped csv.

Data Sources

H2O supports data ingest from various data sources. Natively, a local file system, remote file systems, HDFS, S3, and some relational databases are supported. Additional data sources can be accessed through a generic HDFS API, such as Alluxio or OpenStack Swift.

Default Data Sources

  • Local File System
  • Remote File
  • S3
  • HDFS
  • JDBC
  • Hive

Local File System

Data from a local machine can be uploaded to H2O via a push from the client. For more information, refer to Uploading a File.

Remote File

Data that is hosted on the Internet can be imported into H2O by specifying the URL. For more information, refer to Importing a File.

HDFS-like Data Sources

Various data sources can be accessed through an HDFS API. In this case, a library providing access to a data source needs to be passed on a command line when H2O is launched. (Reminder: Each node in the cluster must be launched in the same way.) The library must be compatible with the HDFS API in order to be registered as a correct HDFS FileSystem.

Alluxio FS

Required Library

To access Alluxio data source, an Alluxio client library that is part of Alluxio distribution is required. For example, alluxio-1.3.0/core/client/target/alluxio-core-client-1.3.0-jar-with-dependencies.jar.

H2O Command Line

java -cp alluxio-core-client-1.3.0-jar-with-dependencies.jar:build/h2o.jar water.H2OApp

URI Scheme

An Alluxio data source is referenced using alluxio:// schema and location of Alluxio master. For example,

alluxio://localhost:19998/iris.csv

core-site.xml Configuration

Not supported.

IBM Swift Object Storage

Required Library

To access IBM Object Store (which can be exposed via Bluemix or Softlayer), IBM’s HDFS driver hadoop-openstack.jar is required. The driver can be obtained, for example, by running BigInsight instances at location /usr/iop/4.2.0.0/hadoop-mapreduce/hadoop-openstack.jar.

Note: The jar available at Maven central is not compatible with IBM Swift Object Storage.

H2O Command Line

java -cp hadoop-openstack.jar:h2o.jar water.H2OApp

URI Scheme

Data source is available under the regular Swift URI structure: swift://<CONTAINER>.<SERVICE>/path/to/file For example,

swift://smalldata.h2o/iris.csv

core-site.xml Configuration

The core-site.xml needs to be configured with Swift Object Store parameters. These are available in the Bluemix/Softlayer management console.

<configuration>
  <property>
    <name>fs.swift.service.SERVICE.auth.url</name>
    <value>https://identity.open.softlayer.com/v3/auth/tokens</value>
  </property>
  <property>
    <name>fs.swift.service.SERVICE.project.id</name>
    <value>...</value>
  </property>
  <property>
    <name>fs.swift.service.SERVICE.user.id</name>
    <value>...</value>
  </property>
  <property>
    <name>fs.swift.service.SERVICE.password</name>
    <value>...</value>
  </property>
  <property>
    <name>fs.swift.service.SERVICE.region</name>
    <value>dallas</value>
  </property>
  <property>
    <name>fs.swift.service.SERVICE.public</name>
    <value>false</value>
  </property>
</configuration>

Google Cloud Storage Connector for Hadoop & Spark

Required Library

To access the Google Cloud Store Object Store, Google’s cloud storage connector, gcs-connector-latest-hadoop2.jar is required. The official documentation and driver can be found here.

H2O Command Line

H2O on Hadoop:
hadoop jar h2o-driver.jar -libjars /path/to/gcs-connector-latest-hadoop2.jar

Sparkling Water
export SPARK_CLASSPATH=/home/nick/spark-2.0.2-bin-hadoop2.6/lib_managed/jar/gcs-connector-latest-hadoop2.jar
sparkling-water-2.0.5/bin/sparkling-shell --conf "spark.executor.memory=10g"

URI Scheme

Data source is available under the regular Google Storage URI structure: gs://<BUCKETNAME>/path/to/file For example,

gs://mybucket/iris.csv

core-site.xml Configuration

core-site.xml must be configured for at least the following properties (class, project-id, bucketname) as shown in the example below. A full list of configuration options is found here.

<configuration>
    <property>
            <name>fs.gs.impl</name>
            <value>com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem</value>
    </property>
    <property>
            <name>fs.gs.project.id</name>
            <value>my-google-project-id</value>
    </property>
    <property>
            <name>fs.gs.system.bucket</name>
            <value>mybucket</value>
    </property>
</configuration>

JDBC Databases

Relational databases that include a JDBC (Java database connectivity) driver can be used as the source of data for machine learning in H2O. Currently supported SQL databases are MySQL, PostgreSQL, MariaDB, Netezza, Amazon Redshift, and Hive. (Refer to Using the Hive 2 JDBC Driver for more information.) Data from these SQL databases can be pulled into H2O using the import_sql_table and import_sql_select functions.

Refer to the following articles for examples about using JDBC data sources with H2O.

import_sql_table

This function imports a SQL table to H2OFrame in memory. This function assumes that the SQL table is not being updated and is stable. Users can run multiple SELECT SQL queries concurrently for parallel ingestion.

Note: Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:

java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp

The import_sql_table function accepts the following parameters:

  • connection_url: The URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, “jdbc:mysql://localhost:3306/menagerie?&useSSL=false”
  • table: The name of the SQL table
  • columns: A list of column names to import from SQL table. Default is to import all columns.
  • username: The username for SQL server
  • password: The password for SQL server
  • optimize: Specifies to optimize the import of SQL table for faster imports. Note that this option is experimental.
connection_url <- "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
table <- "citibike20k"
username <- "root"
password <- "abc123"
my_citibike_data <- h2o.import_sql_table(connection_url, table, username, password)

connection_url = "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
table = "citibike20k"
username = "root"
password = "abc123"
my_citibike_data = h2o.import_sql_table(connection_url, table, username, password)

import_sql_select

This function imports the SQL table that is the result of the specified SQL query to H2OFrame in memory. It creates a temporary SQL table from the specified sql_query. Users can run multiple SELECT SQL queries on the temporary table concurrently for parallel ingestion, and then drop the table.

Note: Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:

java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OApp

The import_sql_select function accepts the following parameters:

  • connection_url: URL of the SQL database connection as specified by the Java Database Connectivity (JDBC) Driver. For example, “jdbc:mysql://localhost:3306/menagerie?&useSSL=false”
  • select_query: SQL query starting with SELECT that returns rows from one or more database tables.
  • username: The username for the SQL server
  • password: The password for the SQL server
  • optimize: Specifies to optimize import of SQL table for faster imports. Note that this option is experimental.
connection_url <- "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
select_query <-  "SELECT  bikeid  from  citibike20k"
username <- "root"
password <- "abc123"
my_citibike_data <- h2o.import_sql_select(connection_url, select_query, username, password)

connection_url = "jdbc:mysql://172.16.2.178:3306/ingestSQL?&useSSL=false"
select_query = "SELECT bikeid from citibike20k"
username = "root"
password = "abc123"
my_citibike_data = h2o.import_sql_select(connection_url, select_query, username, password)

Using the Hive 2 JDBC Driver

H2O can ingest data from Hive through the Hive v2 JDBC driver by providing H2O with the JDBC driver for your Hive version.

Notes:

  • H2O can only load data from Hive v2 due to a limited implementation of the JDBC interface by Hive in earlier versions.
  • This feature is still experimental. In addition, Hive2 support in H2O is not yet suitable for large datasets.

A demo showing how to ingest data from Hive through the Hive v2 JDBC driver is available here. The basic steps are described below.

Retrieve the Hive JDBC Client Jar

Provide H2O with the JDBC Driver

Add the Hive JDBC driver to H2O’s classpath:

java -cp hive-jdbc.jar:<path_to_h2o_jar>: water.H2OApp

Start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:

h2o.init(extra_classpath = "hive-jdbc.jar")

h2o.init(extra_classpath=["hive-jdbc.jar"])

After the jar file with JDBC driver is added, then data from the Hive databases can be pulled into H2O using the aforementioned import_sql_table and import_sql_select functions.