Inserting Data from a Parquet File¶

This guide covers inserting data from Parquet files into SQream DB using FOREIGN TABLE.

In this topic:

1. Prepare the files
2. Place Parquet files where SQream DB workers can access them
3. Figure out the table structure
4. Verify table contents
5. Copying data into SQream DB
- Working around unSupported column types
- Modifying data during the copy process
Further Parquet loading examples
- Loading a table from a directory of Parquet files on HDFS
- Loading a table from a bucket of files on S3

1. Prepare the files ¶

Prepare the source Parquet files, with the following requirements:

SQream DB type → Parquet source	`BOOL`	`SMALLINT`	`INT`	`BIGINT`	`REAL`	`DOUBLE`	`TEXT` 1	`DATETIME`
`BOOLEAN`	Supported
`INT16`		Supported
`INT32`			Supported
`INT64`				Supported
`FLOAT`					Supported
`DOUBLE`						Supported
`BYTE_ARRAY` / `FIXED_LEN_BYTE_ARRAY` 2							Supported
`INT96` 3								Supported 4

If a Parquet file has an unsupported type, such as enum, uuid, time, json, bson, lists, maps, but the data is not referenced in the table (it does not appear in the SELECT query), the statement will succeed. If the column is referenced, an error will be thrown to the user, explaining that the type is not Supported, but the column may be ommited. This can be worked around. See more information in the examples.

Footnotes

1: Text values include TEXT
2: With UTF8 annotation
3: With TIMESTAMP_NANOS or TIMESTAMP_MILLIS annotation
4: Any microseconds will be rounded down to milliseconds.

2. Place Parquet files where SQream DB workers can access them ¶

Any worker may try to access files (unless explicitly speficied with the Workload Manager). It is important that every node has the same view of the storage being used - meaning, every SQream DB worker should have access to the files.

For files hosted on NFS, ensure that the mount is accessible from all servers.
For HDFS, ensure that SQream DB servers can access the HDFS name node with the correct user-id. See our Using SQream in an HDFS Environment guide for more information.
For S3, ensure network access to the S3 endpoint. See our Inserting Data Using Amazon S3 guide for more information.

3. Figure out the table structure ¶

Prior to loading data, you will need to write out the table structure, so that it matches the file structure.

For example, to import the data from nba.parquet, we will first look at the source table:

nba.parquet¶
Name	Team	Number	Position	Age	Height	Weight	College	Salary
Avery Bradley	Boston Celtics	0.0	PG	25.0	6-2	180.0	Texas	7730337.0
Jae Crowder	Boston Celtics	99.0	SF	25.0	6-6	235.0	Marquette	6796117.0
John Holland	Boston Celtics	30.0	SG	27.0	6-5	205.0	Boston University
R.J. Hunter	Boston Celtics	28.0	SG	22.0	6-5	185.0	Georgia State	1148640.0
Jonas Jerebko	Boston Celtics	8.0	PF	29.0	6-10	231.0		5000000.0
Amir Johnson	Boston Celtics	90.0	PF	29.0	6-9	240.0		12000000.0
Jordan Mickey	Boston Celtics	55.0	PF	21.0	6-8	235.0	LSU	1170960.0
Kelly Olynyk	Boston Celtics	41.0	C	25.0	7-0	238.0	Gonzaga	2165160.0
Terry Rozier	Boston Celtics	12.0	PG	22.0	6-2	190.0	Louisville	1824360.0

The file is stored on S3, at s3://sqream-demo-data/nba.parquet.

We will make note of the file structure to create a matching CREATE EXTERNAL TABLE statement.

CREATE FOREIGN TABLE ext_nba
(
     Name       TEXT(40),
     Team       TEXT(40),
     Number     BIGINT,
     Position   TEXT(2),
     Age        BIGINT,
     Height     TEXT(4),
     Weight     BIGINT,
     College    TEXT(40),
     Salary     FLOAT
 )
 WRAPPER parquet_fdw
 OPTIONS
 (
   LOCATION =  's3://sqream-demo-data/nba.parquet'
 );

Tip

Types in SQream DB must match Parquet types exactly.

If the column type isn’t Supported, a possible workaround is to set it to any arbitrary type and then exclude it from subsequent queries.

4. Verify table contents ¶

External tables do not verify file integrity or structure, so verify that the table definition matches up and contains the correct data.

t=> SELECT * FROM ext_nba LIMIT 10;
Name          | Team           | Number | Position | Age | Height | Weight | College           | Salary
--------------+----------------+--------+----------+-----+--------+--------+-------------------+---------
Avery Bradley | Boston Celtics |      0 | PG       |  25 | 6-2    |    180 | Texas             |  7730337
Jae Crowder   | Boston Celtics |     99 | SF       |  25 | 6-6    |    235 | Marquette         |  6796117
John Holland  | Boston Celtics |     30 | SG       |  27 | 6-5    |    205 | Boston University |
R.J. Hunter   | Boston Celtics |     28 | SG       |  22 | 6-5    |    185 | Georgia State     |  1148640
Jonas Jerebko | Boston Celtics |      8 | PF       |  29 | 6-10   |    231 |                   |  5000000
Amir Johnson  | Boston Celtics |     90 | PF       |  29 | 6-9    |    240 |                   | 12000000
Jordan Mickey | Boston Celtics |     55 | PF       |  21 | 6-8    |    235 | LSU               |  1170960
Kelly Olynyk  | Boston Celtics |     41 | C        |  25 | 7-0    |    238 | Gonzaga           |  2165160
Terry Rozier  | Boston Celtics |     12 | PG       |  22 | 6-2    |    190 | Louisville        |  1824360
Marcus Smart  | Boston Celtics |     36 | PG       |  22 | 6-4    |    220 | Oklahoma State    |  3431040

If any errors show up at this stage, verify the structure of the Parquet files and match them to the external table structure you created.

5. Copying data into SQream DB ¶

To load the data into SQream DB, use the CREATE TABLE AS statement:

CREATE TABLE nba AS
   SELECT * FROM ext_nba;

Working around unSupported column types ¶

Suppose you only want to load some of the columns - for example, if one of the columns isn’t Supported.

By ommitting unSupported columns from queries that access the EXTERNAL TABLE, they will never be called, and will not cause a “type mismatch” error.

For this example, assume that the Position column isn’t Supported because of its type.

CREATE TABLE nba AS
   SELECT Name, Team, Number, NULL as Position, Age, Height, Weight, College, Salary FROM ext_nba;

-- We ommitted the unSupported column `Position` from this query, and replaced it with a default ``NULL`` value, to maintain the same table structure.

Modifying data during the copy process ¶

One of the main reasons for staging data with EXTERNAL TABLE is to examine the contents and modify them before loading them.

Assume we are unhappy with weight being in pounds, because we want to use kilograms instead. We can apply the transformation as part of the CREATE TABLE AS statement.

Similar to the previous example, we will also set the Position column as a default NULL.

CREATE TABLE nba AS
   SELECT name, team, number, NULL as position, age, height, (weight / 2.205) as weight, college, salary
           FROM ext_nba
           ORDER BY weight;

Further Parquet loading examples ¶

CREATE FOREIGN TABLE contains several configuration options. See more in the CREATE FOREIGN TABLE parameters section.

Loading a table from a directory of Parquet files on HDFS ¶

CREATE FOREIGN TABLE ext_users
  (id INT NOT NULL, name TEXT(30) NOT NULL, email TEXT(50) NOT NULL)
WRAPPER parquet_fdw
OPTIONS
  (
     LOCATION =  'hdfs://hadoop-nn.piedpiper.com/rhendricks/users/*.parquet'
  );

CREATE TABLE users AS SELECT * FROM ext_users;

Loading a table from a bucket of files on S3 ¶

CREATE FOREIGN TABLE ext_users
  (id INT NOT NULL, name TEXT(30) NOT NULL, email TEXT(50) NOT NULL)
WRAPPER parquet_fdw
OPTIONS
  ( LOCATION = 's3://pp-secret-bucket/users/*.parquet',
    AWS_ID = 'our_aws_id',
    AWS_SECRET = 'our_aws_secret'
   );

CREATE TABLE users AS SELECT * FROM ext_users;