Reading ORC Data

Use the PXF HDFS connector hdfs:orc profile to read ORC-format data when the data resides in a Hadoop file system. This section describes how to read HDFS files that are stored in ORC format, including how to create and query an external table that references these files in the HDFS data store.

The hdfs:orc profile:

  • Reads 1024 rows of data at a time.
  • Supports column projection.
  • Supports filter pushdown based on file-level, stripe-level, and row-level ORC statistics.
  • Supports the compound list type for a subset of ORC scalar types.
  • Does not support the map, union or struct compound types.

The hdfs:orc profile currently supports reading scalar data types and lists of certain scalar types from ORC files. If the data resides in a Hive table, and you want to read complex types or the Hive table is partitioned, use the hive:orc profile.

Prerequisites

Ensure that you have met the PXF Hadoop Prerequisites before you attempt to read data from HDFS.

About the ORC Data Format

The Optimized Row Columnar (ORC) file format is a columnar file format that provides a highly efficient way to both store and access HDFS data. ORC format offers improvements over text and RCFile formats in terms of both compression and performance. PXF supports ORC file versions v0 and v1.

ORC is type-aware and specifically designed for Hadoop workloads. ORC files store both the type of, and encoding information for, the data in the file. All columns within a single group of row data (also known as stripe) are stored together on disk in ORC format files. The columnar nature of the ORC format type enables read projection, helping avoid accessing unnecessary columns during a query.

ORC also supports predicate pushdown with built-in indexes at the file, stripe, and row levels, moving the filter operation to the data loading phase.

Refer to the Apache orc documentation for detailed information about the ORC file format.

Data Type Mapping

To read ORC scalar data types in Greenplum Database, map ORC data values to Greenplum Database columns of the same type. PXF uses the following data type mapping when it reads ORC data:

ORC Physical Type ORC Logical Type PXF/Greenplum Data Type
binary decimal Numeric
binary timestamp Timestamp
byte[] string Text
byte[] char Bpchar
byte[] varchar Varchar
byte[] binary Bytea
Double float Real
Double double Float8
Integer boolean (1 bit) Boolean
Integer tinyint (8 bit) Smallint
Integer smallint (16 bit) Smallint
Integer int (32 bit) Integer
Integer bigint (64 bit) Bigint
Integer date Date

PXF supports only the list ORC compound type, and only for a subset of the ORC scalar types. The supported mappings follow:

ORC Compound Type PXF/Greenplum Data Type
array<string> Text[]
array<char> Bpchar[]
array<varchar> Varchar[]
array<binary> Bytea[]
array<float> Real[]
array<double> Float8[]
array<boolean> Boolean[]
array<tinyint> Smallint[]
array<smallint> Smallint[]
array<int> Integer[]
array<bigint> Bigint[]

Creating the External Table

The PXF HDFS connector hdfs:orc profile supports reading ORC-format HDFS files. Use the following syntax to create a Greenplum Database external table that references a file or directory:

CREATE EXTERNAL TABLE <table_name>
    ( <column_name> <data_type> [, ...] | LIKE <other_table> )
LOCATION ('pxf://<path-to-hdfs-file>
    ?PROFILE=hdfs:orc[&SERVER=<server_name>][&<custom-option>=<value>[...]]')
FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import')

The specific keywords and values used in the Greenplum Database CREATE EXTERNAL TABLE command are described below.

Keyword Value
<path‑to‑hdfs‑file> The path to the file or directory in the HDFS data store. When the <server_name> configuration includes a pxf.fs.basePath property setting, PXF considers <path‑to‑hdfs‑file> to be relative to the base path specified. Otherwise, PXF considers it to be an absolute path. <path‑to‑hdfs‑file> must not specify a relative path nor include the dollar sign ($) character.
PROFILE The PROFILE keyword must specify hdfs:orc.
SERVER=<server_name> The named server configuration that PXF uses to access the data. PXF uses the default server if not specified.
<custom-option> <custom-option>s are described below.
FORMAT Use FORMAT 'CUSTOM'; the CUSTOM format requires the built-in pxfwritable_import formatter.

The PXF hdfs:orc profile supports the following read options. You specify this option in the LOCATION clause:

Read Option Value Description
IGNORE_MISSING_PATH A Boolean value that specifies the action to take when <path-to-hdfs-file> is missing or invalid. The default value is false, PXF returns an error in this situation. When the value is true, PXF ignores missing path errors and returns an empty fragment.
MAP_BY_POSITION A Boolean value that, when set to true, specifies that PXF should map an ORC column to a Greenplum Database column by position. The default value is false, PXF maps an ORC column to a Greenplum column by name.

Example: Reading an ORC File on HDFS

This example operates on a simple data set that models a retail sales operation. The data includes fields with the following names and types:

Column Name Data Type
location text
month text
num_orders integer
total_sales numeric(10,2)
items_sold text[]

In this example, you:

  • Create a sample data set in JSON format, use the orc-tools JAR utilities to convert the JSON file into an ORC-format file, and then copy the ORC file to HDFS.
  • Create a Greenplum Database readable external table that references the ORC file and that specifies the hdfs:orc profile.
  • Query the external table.

You must have administrative privileges to both a Hadoop cluster and a Greenplum Database cluster to run the example. You must also have configured a PXF server to access Hadoop.

Procedure:

  1. Create a JSON file named sampledata.json in the /tmp directory:

    hdfsclient$ echo '{"location": "Prague", "month": "Jan","num_orders": 101, "total_sales": 4875.33, "items_sold": ["boots", "hats"]}
    {"location": "Rome", "month": "Mar","num_orders": 87, "total_sales": 1557.39, "items_sold": ["coats"]}
    {"location": "Bangalore", "month": "May","num_orders": 317, "total_sales": 8936.99, "items_sold": ["winter socks", "long-sleeved shirts", "boots"]}
    {"location": "Beijing", "month": "Jul","num_orders": 411, "total_sales": 11600.67, "items_sold": ["hoodies/sweaters", "pants"]}
    {"location": "Los Angeles", "month": "Dec","num_orders": 0, "total_sales": 0.00, "items_sold": null}
    ' > /tmp/sampledata.json
    
  2. Download the orc-tools JAR.

  3. Run the orc-tools convert command to convert sampledata.json to the ORC file /tmp/sampledata.orc; provide the schema to the command:

    hdfsclient$ java -jar orc-tools-1.6.2-uber.jar convert /tmp/sampledata.json \
      --schema 'struct<location:string,month:string,num_orders:int,total_sales:decimal(10,2),items_sold:array<string>>' \
      -o /tmp/sampledata.orc
    
  4. Copy the ORC file to HDFS. The following command copies the file to the /data/pxf_examples directory:

    hdfsclient$ hdfs dfs -put /tmp/sampledata.orc /data/pxf_examples/
    
  5. Log in to the Greenplum Database master host and connect to a database. This command connects to the database named testdb as the gpadmin user:

    gpadmin@gpmaster$ psql -d testdb
    
  6. Create an external table named sample_orc that references the /data/pxf_examples/sampledata.orc file on HDFS. This command creates the table with the column names specified in the ORC schema, and uses the default PXF server:

    testdb=# CREATE EXTERNAL TABLE sample_orc(location text, month text, num_orders int, total_sales numeric(10,2), items_sold text[])
               LOCATION ('pxf://data/pxf_examples/sampledata.orc?PROFILE=hdfs:orc')
             FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');
    
  7. Read the data in the file by querying the sample_orc table:

    testdb=# SELECT * FROM sample_orc;
    
      location   | month | num_orders | total_sales |                  items_sold
    -------------+-------+------------+-------------+----------------------------------------------
     Prague      | Jan   |        101 |     4875.33 | {boots,hats}
     Rome        | Mar   |         87 |     1557.39 | {coats}
     Bangalore   | May   |        317 |     8936.99 | {"winter socks","long-sleeved shirts",boots}
     Beijing     | Jul   |        411 |    11600.67 | {hoodies/sweaters,pants}
     Los Angeles | Dec   |          0 |        0.00 |
    (5 rows)
    
  8. You can query the data on any column, including the items_sold array column. For example, this query returns the rows where the items sold include boots and/or pants:

    testdb=# SELECT * FROM sample_orc WHERE items_sold && '{"boots", "pants"}';
    
     location  | month | num_orders | total_sales |                  items_sold
    -----------+-------+------------+-------------+----------------------------------------------
     Prague    | Jan   |        101 |     4875.33 | {boots,hats}
     Bangalore | May   |        317 |     8936.99 | {"winter socks","long-sleeved shirts",boots}
     Beijing   | Jul   |        411 |    11600.67 | {hoodies/sweaters,pants}
    (3 rows)
    
  9. This query returns the rows where the first item sold is boots:

    testdb=# SELECT * FROM sample_orc WHERE items_sold[0] = 'boots';
    
     location  | month | num_orders | total_sales |                  items_sold
    -----------+-------+------------+-------------+----------------------------------------------
     Prague    | Jan   |        101 |     4875.33 | {boots,hats}
    (1 row)