Reading Data from HDFS with PXF

HDFS is the primary distributed storage mechanism used by Apache Hadoop applications. The PXF HDFS connector reads file data stored in HDFS. The connector supports plain delimited and comma-separated-value format data. The HDFS connector also supports JSON and the Avro binary format.

This section describes how to use PXF to access HDFS data, including how to create and query an external table referencing files in the HDFS data store.

Prerequisites

Before working with HDFS data using PXF, ensure that:

• You have installed and configured a Hadoop client on each Greenplum Database segment host. Refer to Installing and Configuring Hadoop Clients for PXF for instructions. If you plan to access JSON format data stored in a Cloudera Hadoop cluster, PXF requires a Cloudera version 5.8 or later Hadoop distribution.
• You have initialized PXF on your Greenplum Database segment hosts, and PXF is running on each host. See Configuring, Initializing, and Managing PXF for PXF initialization, configuration, and startup information.
• You have granted the gpadmin user read permission on the relevant portions of your HDFS file system.

HDFS Data Formats

The PXF HDFS connector supports reading the following data formats:

• Text - comma-separated value (.csv) or delimited format plain text data
• Avro - JSON-defined, schema-based data serialization format
• JSON - JSON format data

The PXF HDFS connector provides the following profiles to read the data formats listed above:

HDFS Shell Command Primer

Hadoop includes command-line tools that interact directly with your HDFS file system. These tools support typical file system operations including copying and listing files, changing file permissions, and so forth.

The HDFS file system command syntax is hdfs dfs <options> [<file>]. Invoked with no options, hdfs dfs lists the file system options supported by the tool.

The user invoking the hdfs dfs command must have read privileges on the HDFS data store to list and view directory and file contents, and write permission to create directories and files.

The hdfs dfs options used in this topic are:

Examples:

Create a directory in HDFS:

$ hdfs dfs -mkdir -p /data/exampledir

Copy a text file from your local file system to HDFS:

$ hdfs dfs -put /tmp/example.txt /data/exampledir/

Display the contents of a text file located in HDFS:

$ hdfs dfs -cat /data/exampledir/example.txt

Querying External HDFS Data

The PXF HDFS connector supports the HdfsTextSimple, HdfsTextMulti, and Avro profiles.

Use the following syntax to create a Greenplum Database readable external table referencing HDFS data: 

CREATE EXTERNAL TABLE <table_name> 
    ( <column_name> <data_type> [, ...] | LIKE <other_table> )
LOCATION ('pxf://<path-to-hdfs-file>
    ?PROFILE=HdfsTextSimple|HdfsTextMulti|Avro|Json[&<custom-option>=<value>[...]]')
FORMAT '[TEXT|CSV|CUSTOM]' (<formatting-properties>);

The specific keywords and values used by the pxf protocol in the CREATE EXTERNAL TABLE command are described in the table below.

Note: When creating PXF external tables, you cannot use the HEADER option in your FORMAT specification.

Reading Text Data

Use the HdfsTextSimple profile when you read a plain text delimited or .csv data where each row is a single record.

<formatting-properties> supported by the HdfsTextSimple profile include:

Example: Using the HdfsTextSimple Profile

Perform the following procedure to create a sample text file, copy the file to HDFS, and use the HdfsTextSimple profile to create two PXF external tables to query the data:

1. Create an HDFS directory for PXF example data files. For example:

   $ hdfs dfs -mkdir -p /data/pxf_examples
   

2. Create a delimited plain text data file named pxf_hdfs_simple.txt:

   $ echo 'Prague,Jan,101,4875.33
   Rome,Mar,87,1557.39
   Bangalore,May,317,8936.99
   Beijing,Jul,411,11600.67' > /tmp/pxf_hdfs_simple.txt
   

   Note the use of the comma , to separate the four data fields.

3. Add the data file to HDFS:

   $ hdfs dfs -put /tmp/pxf_hdfs_simple.txt /data/pxf_examples/
   

4. Display the contents of the pxf_hdfs_simple.txt file stored in HDFS:

   $ hdfs dfs -cat /data/pxf_examples/pxf_hdfs_simple.txt
   

5. Start the psql subsystem:

   $ psql -d postgres
   

6. Use the PXF HdfsTextSimple profile to create a Greenplum Database external table referencing the pxf_hdfs_simple.txt file you just created and added to HDFS:

   postgres=# CREATE EXTERNAL TABLE pxf_hdfs_textsimple(location text, month text, num_orders int, total_sales float8)
               LOCATION ('pxf://data/pxf_examples/pxf_hdfs_simple.txt?PROFILE=HdfsTextSimple')
             FORMAT 'TEXT' (delimiter=E',');
   

7. Query the external table:

   postgres=# SELECT * FROM pxf_hdfs_textsimple;          
   

      location    | month | num_orders | total_sales 
   ---------------+-------+------------+-------------
    Prague        | Jan   |        101 |     4875.33
    Rome          | Mar   |         87 |     1557.39
    Bangalore     | May   |        317 |     8936.99
    Beijing       | Jul   |        411 |    11600.67
   (4 rows)
   

8. Create a second external table referencing pxf_hdfs_simple.txt, this time specifying the CSV FORMAT:

   postgres=# CREATE EXTERNAL TABLE pxf_hdfs_textsimple_csv(location text, month text, num_orders int, total_sales float8)
               LOCATION ('pxf://data/pxf_examples/pxf_hdfs_simple.txt?PROFILE=HdfsTextSimple')
             FORMAT 'CSV';
   postgres=# SELECT * FROM pxf_hdfs_textsimple_csv;          
   

   When specifying FORMAT 'CSV' for comma-separated value data no delimiter formatter option is required because comma is the default delimiter value.

Reading Text Data with Quoted Linefeeds

Use the HdfsTextMulti profile to read plain text data with delimited single- or multi- line records that include embedded (quoted) linefeed characters.

<formatting-properties> supported by the HdfsTextMulti profile include:

Example: Using the HdfsTextMulti Profile

Perform the following steps to create a sample text file, copy the file to HDFS, and use the PXF HdfsTextMulti profile to create a Greenplum Database readable external table to query the data:

1. Create a second delimited plain text file:

   $ vi /tmp/pxf_hdfs_multi.txt
   

2. Copy/paste the following data into pxf_hdfs_multi.txt:

   "4627 Star Rd.
   San Francisco, CA  94107":Sept:2017
   "113 Moon St.
   San Diego, CA  92093":Jan:2018
   "51 Belt Ct.
   Denver, CO  90123":Dec:2016
   "93114 Radial Rd.
   Chicago, IL  60605":Jul:2017
   "7301 Brookview Ave.
   Columbus, OH  43213":Dec:2018
   

   Notice the use of the colon : to separate the three fields. Also notice the quotes around the first (address) field. This field includes an embedded line feed separating the street address from the city and state.

3. Copy the text file to HDFS:

   $ hdfs dfs -put /tmp/pxf_hdfs_multi.txt /data/pxf_examples/
   

4. Use the HdfsTextMulti profile to create an external table referencing the pxf_hdfs_multi.txt HDFS file, making sure to identify the : (colon) as the field separator:

   postgres=# CREATE EXTERNAL TABLE pxf_hdfs_textmulti(address text, month text, year int)
               LOCATION ('pxf://data/pxf_examples/pxf_hdfs_multi.txt?PROFILE=HdfsTextMulti')
             FORMAT 'CSV' (delimiter ':');
   

   Notice the alternate syntax for specifying the delimiter.

5. Query the pxf_hdfs_textmulti table:

   postgres=# SELECT * FROM pxf_hdfs_textmulti;
   

            address          | month | year 
   --------------------------+-------+------
    4627 Star Rd.            | Sept  | 2017
    San Francisco, CA  94107           
    113 Moon St.             | Jan   | 2018
    San Diego, CA  92093               
    51 Belt Ct.              | Dec   | 2016
    Denver, CO  90123                  
    93114 Radial Rd.         | Jul   | 2017
    Chicago, IL  60605                 
    7301 Brookview Ave.      | Dec   | 2018
    Columbus, OH  43213                
   (5 rows)
   

Reading Avro Data

Apache Avro is a data serialization framework where the data is serialized in a compact binary format. Avro specifies that data types be defined in JSON. Avro format data has an independent schema, also defined in JSON. An Avro schema, together with its data, is fully self-describing.

Data Type Mapping

Avro supports both primitive and complex data types.

To represent Avro primitive data types in Greenplum Database, map data values to Greenplum Database columns of the same type.

Avro supports complex data types including arrays, maps, records, enumerations, and fixed types. Map top-level fields of these complex data types to the Greenplum Database TEXT type. While Greenplum Database does not natively support these types, you can create Greenplum Database functions or application code to extract or further process subcomponents of these complex data types.

The following table summarizes external mapping rules for Avro data.

Avro-Specific Options

For complex types, the PXF Avro profile inserts default delimiters between collection items and values. You can use non-default delimiter characters by identifying values for specific Avro custom options in the CREATE EXTERNAL TABLE command.

The Avro profile supports the following <custom-option>s:

The <formatting-properties> supported by the Avro profile include:

Avro Schemas and Data

Avro schemas are defined using JSON, and composed of the same primitive and complex types identified in the data type mapping section above. Avro schema files typically have a .avsc suffix.

Fields in an Avro schema file are defined via an array of objects, each of which is specified by a name and a type.

Example: Using the Avro Profile

The examples in this section will operate on Avro data with the following field name and data type record schema:

• id - long
• username - string
• followers - array of string
• fmap - map of long
• relationship - enumerated type
• address - record comprised of street number (int), street name (string), and city (string)

Create Schema

Perform the following operations to create an Avro schema to represent the example schema described above.

1. Create a file named avro_schema.avsc:

   $ vi /tmp/avro_schema.avsc
   

2. Copy and paste the following text into avro_schema.avsc:

   {
   "type" : "record",
     "name" : "example_schema",
     "namespace" : "com.example",
     "fields" : [ {
       "name" : "id",
       "type" : "long",
       "doc" : "Id of the user account"
     }, {
       "name" : "username",
       "type" : "string",
       "doc" : "Name of the user account"
     }, {
       "name" : "followers",
       "type" : {"type": "array", "items": "string"},
       "doc" : "Users followers"
     }, {
       "name": "fmap",
       "type": {"type": "map", "values": "long"}
     }, {
       "name": "relationship",
       "type": {
           "type": "enum",
           "name": "relationshipEnum",
           "symbols": ["MARRIED","LOVE","FRIEND","COLLEAGUE","STRANGER","ENEMY"]
       }
     }, {
       "name": "address",
       "type": {
           "type": "record",
           "name": "addressRecord",
           "fields": [
               {"name":"number", "type":"int"},
               {"name":"street", "type":"string"},
               {"name":"city", "type":"string"}]
       }
     } ],
     "doc:" : "A basic schema for storing messages"
   }
   

Create Avro Data File (JSON)

Perform the following steps to create a sample Avro data file conforming to the above schema.

1. Create a text file named pxf_hdfs_avro.txt:

   $ vi /tmp/pxf_hdfs_avro.txt
   

2. Enter the following data into pxf_hdfs_avro.txt:

   {"id":1, "username":"john","followers":["kate", "santosh"], "relationship": "FRIEND", "fmap": {"kate":10,"santosh":4}, "address":{"number":1, "street":"renaissance drive", "city":"san jose"}}
   
   {"id":2, "username":"jim","followers":["john", "pam"], "relationship": "COLLEAGUE", "fmap": {"john":3,"pam":3}, "address":{"number":9, "street":"deer creek", "city":"palo alto"}}
   

   The sample data uses a comma , to separate top level records and a colon : to separate map/key values and record field name/values.

3. Convert the text file to Avro format. There are various ways to perform the conversion, both programmatically and via the command line. In this example, we use the Java Avro tools; the jar avro-tools-1.8.1.jar file resides in the current directory:

   $ java -jar ./avro-tools-1.8.1.jar fromjson --schema-file /tmp/avro_schema.avsc /tmp/pxf_hdfs_avro.txt > /tmp/pxf_hdfs_avro.avro
   

   The generated Avro binary data file is written to /tmp/pxf_hdfs_avro.avro.

4. Copy the generated Avro file to HDFS:

   $ hdfs dfs -put /tmp/pxf_hdfs_avro.avro /data/pxf_examples/
   

Query With Avro Profile

Perform the following operations to create and query an external table referencing the pxf_hdfs_avro.avro file that you added to HDFS in the previous section. When creating the table:

• Map the top-level primitive fields, id (type long) and username (type string), to their equivalent Greenplum Database types (bigint and text).
• Map the remaining complex fields to type text.
• Explicitly set the record, map, and collection delimiters using the Avro profile custom options.

1. Use the Avro profile to create a queryable external table from the pxf_hdfs_avro.avro file:

   postgres=# CREATE EXTERNAL TABLE pxf_hdfs_avro(id bigint, username text, followers text, fmap text, relationship text, address text)
               LOCATION ('pxf://data/pxf_examples/pxf_hdfs_avro.avro?PROFILE=Avro&COLLECTION_DELIM=,&MAPKEY_DELIM=:&RECORDKEY_DELIM=:')
             FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');
   

2. Perform a simple query of the pxf_hdfs_avro table:

   postgres=# SELECT * FROM pxf_hdfs_avro;
   

    id | username |   followers    |        fmap         | relationship |                      address                      
   ----+----------+----------------+--------------------+--------------+---------------------------------------------------
     1 | john     | [kate,santosh] | {kate:10,santosh:4} | FRIEND       | {number:1,street:renaissance drive,city:san jose}
     2 | jim      | [john,pam]     | {pam:3,john:3}      | COLLEAGUE    | {number:9,street:deer creek,city:palo alto}
   (2 rows)
   

   The simple query of the external table shows the components of the complex type data separated with the delimiters specified in the CREATE EXTERNAL TABLE call.

3. Process the delimited components in the text columns as necessary for your application. For example, the following command uses the Greenplum Database internal string_to_array function to convert entries in the followers field to a text array column in a new view.

   postgres=# CREATE VIEW followers_view AS 
   SELECT username, address, string_to_array(substring(followers FROM 2 FOR (char_length(followers) - 2)), ',')::text[] 
       AS followers 
     FROM pxf_hdfs_avro;
   

4. Query the view to filter rows based on whether a particular follower appears in the view:

   postgres=# SELECT username, address FROM followers_view WHERE followers @> '{john}';
   

    username |                   address                   
   ----------+---------------------------------------------
    jim      | {number:9,street:deer creek,city:palo alto}
   

Reading JSON Data

Use the Json profile when you want to read native JSON format data from HDFS.

Working with JSON Data

JSON is a text-based data-interchange format. JSON data is typically stored in a file with a .json suffix.

A .json file will contain a collection of objects. A JSON object is a collection of unordered name/value pairs. A value can be a string, a number, true, false, null, or an object or an array. You can define nested JSON objects and arrays.

Sample JSON data file content:

  {
    "created_at":"MonSep3004:04:53+00002013",
    "id_str":"384529256681725952",
    "user": {
      "id":31424214,
      "location":"COLUMBUS"
    },
    "coordinates":{
      "type":"Point",
      "values":[
         13,
         99
      ]
    }
  }

In the sample above, user is an object composed of fields named id and location. To specify the nested fields in the user object as Greenplum Database external table columns, use . projection:

user.id
user.location

coordinates is an object composed of a text field named type and an array of integers named values. Use [] to identify specific elements of the values array as Greenplum Database external table columns:

coordinates.values[0]
coordinates.values[1]

Refer to Introducing JSON for detailed information on JSON syntax.

JSON to Greenplum Database Data Type Mapping

To represent JSON data in Greenplum Database, map data values that use a primitive data type to Greenplum Database columns of the same type. JSON supports complex data types including projections and arrays. Use N-level projection to map members of nested objects and arrays to primitive data types.

The following table summarizes external mapping rules for JSON data.

Table 1. JSON Mapping

JSON Data Read Modes

PXF supports two data read modes. The default mode expects one full JSON record per line. PXF also supports a read mode operating on JSON records that span multiple lines.

In upcoming examples, you will use both read modes to operate on a sample data set. The schema of the sample data set defines objects with the following member names and value data types:

• “created_at” - text
• “id_str” - text
• “user” - object
  • “id” - integer
  • “location” - text
• “coordinates” - object (optional)
  • “type” - text
  • “values” - array
    • [0] - integer
    • [1] - integer

The single-JSON-record-per-line data set follows:

{"created_at":"FriJun0722:45:03+00002013","id_str":"343136551322136576","user":{
"id":395504494,"location":"NearCornwall"},"coordinates":{"type":"Point","values"
: [ 6, 50 ]}},
{"created_at":"FriJun0722:45:02+00002013","id_str":"343136547115253761","user":{
"id":26643566,"location":"Austin,Texas"}, "coordinates": null},
{"created_at":"FriJun0722:45:02+00002013","id_str":"343136547136233472","user":{
"id":287819058,"location":""}, "coordinates": null}

This is the data set for for the multi-line JSON record data set:

{
  "root":[
    {
      "record_obj":{
        "created_at":"MonSep3004:04:53+00002013",
        "id_str":"384529256681725952",
        "user":{
          "id":31424214,
          "location":"COLUMBUS"
        },
        "coordinates":null
      },
      "record_obj":{
        "created_at":"MonSep3004:04:54+00002013",
        "id_str":"384529260872228864",
        "user":{
          "id":67600981,
          "location":"KryberWorld"
        },
        "coordinates":{
          "type":"Point",
          "values":[
             8,
             52
          ]
        }
      }
    }
  ]
}

You will create JSON files for the sample data sets and add them to HDFS in the next section.

Loading the Sample JSON Data to HDFS

The PXF HDFS connector reads native JSON stored in HDFS. Before you can use Greenplum Database to query JSON format data, the data must reside in your HDFS data store.

Copy and paste the single line JSON record sample data set above to a file named singleline.json. Similarly, copy and paste the multi-line JSON record data set to a file named multiline.json.

Note: Ensure that there are no blank lines in your JSON files.

Copy the JSON data files you just created to your HDFS data store. Create the /data/pxf_examples directory if you did not do so in a previous exercise. For example:

$ hdfs dfs -mkdir /data/pxf_examples
$ hdfs dfs -put singleline.json /data/pxf_examples
$ hdfs dfs -put multiline.json /data/pxf_examples

Once the data is loaded to HDFS, you can use Greenplum Database and PXF to query and analyze the JSON data.

Custom Options

PXF supports single- and multi- line JSON records. When you want to read multi-line JSON records, you must provide an IDENTIFIER <custom-option> and value. Use this <custom-option> to identify the member name of the first field in the JSON record object:

Example: Single Line JSON Records

Use the following CREATE EXTERNAL TABLE SQL command to create a readable external table that references the single-line-per-record JSON data file.

CREATE EXTERNAL TABLE singleline_json_tbl(
  created_at TEXT,
  id_str TEXT,
  "user.id" INTEGER,
  "user.location" TEXT,
  "coordinates.values[0]" INTEGER,
  "coordinates.values[1]" INTEGER
)
LOCATION('pxf://data/pxf_examples/singleline.json?PROFILE=Json')
FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

Notice the use of . projection to access the nested fields in the user and coordinates objects. Also notice the use of [] to access specific elements of the coordinates.values[] array.

To query the JSON data in the external table:

SELECT * FROM singleline_json_tbl;

Example: Multi-Line Records

The SQL command to create a readable external table from the multi-line-per-record JSON file is very similar to that of the single line data set above. You must additionally specify the LOCATION clause IDENTIFIER keyword and an associated value when you want to read multi-line JSON records. For example:

CREATE EXTERNAL TABLE multiline_json_tbl(
  created_at TEXT,
  id_str TEXT,
  "user.id" INTEGER,
  "user.location" TEXT,
  "coordinates.values[0]" INTEGER,
  "coordinates.values[1]" INTEGER
)
LOCATION('pxf://data/pxf_examples/multiline.json?PROFILE=Json&IDENTIFIER=created_at')
FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');

created_at identifies the member name of the first field in the JSON record record_obj in the sample data schema.

To query the JSON data in this external table:

SELECT * FROM multiline_json_tbl;