Importing CSV Files

Deephaven provides tools for inferring a table schema from sample data and importing CSV files.

Quickstart

Here is an example of how to generate a schema from a CSV data file, deploy it, and import a file from the command line. These commands assume a typical Deephaven installation and a sample file located at /data/sample.csv.

Generate a schema from a sample CSV data file

iris_exec csv_schema_creator -- -ns CSVExampleNamespace -tn CSVExampleTableName -sf /data/sample.csv

This creates CSVExampleNamespace.CSVExampleTableName.schema in a folder named "CSVExampleNamespace".

Deploy the schema

sudo -u irisadmin /usr/illumon/latest/bin/dhconfig schema import --file /etc/sysconfig/illumon.d/schema/CSVExampleNamespace/CSVExampleNamespace.CSVExampleTableName.schema

If the schema is in a different location, update the directory accordingly.

Import a single data file into the specified Intraday partition

sudo -u dbmerge /usr/illumon/latest/bin/iris_exec csv_import -- -ns CSVExampleNamespace -tn CSVExampleTableName -sf /data/sample.csv -dp localhost/2018-09-26

This example will generate a table schema from sample.csv with the namespace "CSVExampleNamespace" and table name "CSVExampleTableName". The default behavior creates a directory for the namespace in the current directory and places the output schema file inside this directory. After inspection of the generated schema, it may be deployed. You might want to move this schema file to a standard location for reference. Once deployed, CSV files matching the structure of sample.csv can be imported.

Schema Inference

CSV schema inference generates a table schema from a sample CSV file. A table column is generated for each column in the given sample file, and the process attempts to guess the type for each column from the provided data. Since CSV files provide no metadata, this inference process is necessarily imperfect, and generated schemas should always be inspected before deployment.

Command Reference

iris_exec csv_schema_creator <launch args> -- <schema creator args>

The following arguments are available when running the CSV schema creator:

Import Query

A persistent query of type "Import - CSV" is used to import data from a CSV file into an intraday partition. The CSV file must be available on the server on which the query will run; it is not for loading a file on the user's system into Deephaven.

When Import - CSV is selected, the Persistent Query Configuration Editor window shows the following options:

• To proceed with creating a query to import a CSV file, you will need to select a DB Server and enter the desired value for Memory (Heap) Usage (GB).

• Options available in the Show Advanced Options section of the panel are typically not used when importing or merging data. To learn more about this section, please refer to the Persistent Query Configuration Viewer/Editor.

• The Access Control tab presents a panel with the same options as all other configuration types, and gives the query owner the ability to authorize Admin and Viewer Groups for this query. For more information, please refer to Access Control.

• Clicking the Scheduling tab presents a panel with the same scheduling options as all other configuration types. For more information, please refer to Scheduling.

• Clicking the CsvImport Settings tab presents a panel with the options pertaining to importing a CSV file:

  

CsvImport Settings

• Namespace: This is the namespace into which you want to import the file.
• Table: This is the table into which you want to import the data.
• Output Mode: This determines what happens if data is found in the fully-specified partition for the data. The fully-specified partition includes both the internal partition (unique for the import job) and the column partition (usually the date).
  • Safe - if existing data is found in the fully-specified partition, the import job will fail.
  • Append - if existing data is found in the fully-specified partition, data will be appended to it.
  • Replace - if existing data is found in the fully-specified partition, it will be replaced. This does not replace all data for a column partition value, just the data in the fully-specified partition. Import Source: This is the import source section of the associated schema file that specifies how source data columns will be set as Deephaven columns.
  • Strict Import will fail if a file being imported has missing column values (nulls), unless those columns allow or replace the null values using a default attribute in the ImportColumn definition.
• Single/Multi Partition: This controls the import mode. In single-partition, all of the data is imported into a single Intraday partition. In multi-partition mode, you must specify a column in the source data that will control to which partition each row is imported.
  • Single-partition configuration:
    • Partition Formula: This is the formula needed to partition the CSV being imported. If a specific partition value is used it will need to be surrounded by quotes. For example: currentDateNy() "2017-01-01"
    • Partition Substitution: This is a token used to substitute the determined column partition value in the source directory, source file, or source glob, to allow the dynamic determination of these fields. For example, if the partition substitution is "PARTITION_SUB", and the source directory includes "PARTITION_SUB" in its value, that PARTITION_SUB will be replaced with the partition value determined from the partition formula.
    • Substitution Date Format: This is the date format that will be used when a Partition Substitution is used. The standard Deephaven date partition format is yyyy-MM-dd (e.g., 2018-05-30), but this allows substitution in another format. For example, if the filename includes the date in yyyyddMM format instead (e.g., 20183005), that could be used in the Date Substitution Format field. All the patterns from the Java DateTimeFormatter class are allowed.
  • Multi-partition configuration:
    • Import Partition Column: This is the name of the database column used to choose the target partition for each row (typically "Date"). There must be an corresponding Import Column present in the schema, which will indicate how to get this value from the source data.
• File Format: This is the format of the data in the CSV file being imported. Options include DEFAULT, TRIM, EXCEL, TDF, MYSQL, RFC4180, and BPIPE*.
• Delimiter: This can be used to specify a custom delimiter character if something other than a comma is used in the file.
• Source Directory: This is the path to where the CSV file is stored on the server on which the query will run.
• Source File: This the name of the CSV file to import.
• Source Glob: This is an expression used to match multiple CSV file names.
• Constant Value: A String of data to make available as a pseudo-column to fields using the CONSTANT sourceType.

* Note: BPIPE is the format used for Bloomberg's Data License product.

Importing Using Builder

CSV imports may be performed from inside a Deephaven Groovy or Python script. Compared to other methods, this permits more elaborate logic with respect to existing data. These scripts may be executed as a persistent query or from the command line using the iris_exec run_local_script tool. All imports should be performed as the dbmerge user (or from a persistent query, running on a merge server).

Example

The following script imports a single CSV file to a specified partition:

import com.illumon.iris.importers.util.CsvImport
import com.illumon.iris.importers.ImportOutputMode

rows = new CsvImport.Builder("Test","Sample")
    .setSourceFile("/db/TempFiles/dbquery/staging/data1.csv")
    .setDestinationPartitions("localhost/2018-04-01")
    .setOutputMode(ImportOutputMode.REPLACE)
    .build()
    .run()

println "Imported " + rows + " rows."

from deephaven import *

rows = CsvImport.Builder("Test", "Sample")\
    .setSourceFile("/db/TempFiles/dbquery/staging/data1.csv")\
    .setDestinationPartitions("localhost/2018-04-01")\
    .setOutputMode("REPLACE")\
    .build()\
    .run()

print("Imported {} rows.".format(rows))

Import API Reference

The CSV import class provides a static builder method, which produces an object used to set parameters for the import. The builder returns an import object from the build() method. Imports are executed via the run() method and if successful, return the number of rows imported. All other parameters and options for the import are configured via the setter methods described below. The general pattern when scripting an import is:

nRows = CsvImport.builder(<namespace>,<table>)
    .set<option>(<option value>)
    ...
    .build()
    .run()

CSV Import Options

* The sourceDirectory parameter will be used in conjunction with sourceFile or sourceGlob. If sourceDirectory is not provided, but sourceFile is, then sourceFile will be used as a fully qualified file name. If sourceDirectory is not provided, but sourceGlob is, then sourceDirectory will default to the configured log file directory from the prop file being used.

Import from Command Line

CSV imports can be performed directly from the command line, using the iris_exec tool.

Command Reference

iris_exec csv_import <launch args> -- <csv import args>

CSV Import Arguments

The CSV importer takes the following arguments:

One special case when importing CSV data is columns in the target table with a type of Boolean.

• By default, the CSV importer will attempt to interpret string data from the source table with 0, F, f, or any case of false, being written as a Boolean false.
• Similarly, 1, T, t, or any case of true would be a Boolean true.
• Any other value would result in a conversion failure that may be continuable, if, for instance, a default is set for the column.
• To convert from other representations of true/false, for example from foreign language strings, a formula or transform would be needed, along with a sourceType="String" to ensure the reading of the data from the CSV field is handled correctly.