Foreign Tables

Foreign tables can be used to run queries directly on data without inserting it into SQream DB first.

SQream DB supports read only foreign tables, so you can query from foreign tables, but you cannot insert to them, or run deletes or updates on them. Running queries directly on foreign data is most effectively used for things like one off querying. If you will be repeatedly querying data, the performance will usually be better if you insert the data into SQream DB first.

Although foreign tables can be used without inserting data into SQream DB, one of their main use cases is to help with the insertion process. An insert select statement on a foreign table can be used to insert data into SQream using the full power of the query engine to perform ETL.

What Kind of Data is Supported?

SQream DB supports foreign tables over:

  • text files (e.g. CSV, PSV, TSV)

  • ORC

  • Parquet

What Kind of Data Staging is Supported?

SQream DB can stage data from:

Using foreign tables - a practical example

Use a foreign table to stage data before loading from CSV, Parquet or ORC files.

Planning for Data Staging

For the following examples, we will want to interact with a CSV file. Here’s a peek at the table contents:

nba-t10

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 Inserting Data Using Amazon S3, at s3://sqream-demo-data/nba_players.csv. We will make note of the file structure, to create a matching CREATE_FOREIGN_TABLE statement.

Creating the foreign table

Based on the source file structure, we we create a foreign table with the appropriate structure, and point it to the file.

CREATE FOREIGN TABLE nba
(
   Name text,
   Team text,
   Number tinyint,
   Position text,
   Age tinyint,
   Height text,
   Weight real,
   College text,
   Salary float
 )
   USING FORMAT CSV -- Text file
   WITH  PATH  's3://sqream-demo-data/nba_players.csv'
   RECORD DELIMITER '\r\n'; -- DOS delimited file

The file format in this case is CSV, and it is stored as an Inserting Data Using Amazon S3 object (if the path is on Using SQream in an HDFS Environment, change the URI accordingly). We also took note that the record delimiter was a DOS newline (\r\n).

Querying foreign tables

Let’s peek at the data from the foreign table:

t=> SELECT * FROM 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

Modifying Data from Staging

One of the main reasons for staging data 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 a query:

t=> SELECT name, team, number, position, age, height, (weight / 2.205) as weight, college, salary
.          FROM nba
.          ORDER BY weight;

name                     | team                   | number | position | age | height | weight   | college               | salary
-------------------------+------------------------+--------+----------+-----+--------+----------+-----------------------+---------
Nikola Pekovic           | Minnesota Timberwolves |     14 | C        |  30 | 6-11   |  139.229 |                       | 12100000
Boban Marjanovic         | San Antonio Spurs      |     40 | C        |  27 | 7-3    | 131.5193 |                       |  1200000
Al Jefferson             | Charlotte Hornets      |     25 | C        |  31 | 6-10   | 131.0658 |                       | 13500000
Jusuf Nurkic             | Denver Nuggets         |     23 | C        |  21 | 7-0    | 126.9841 |                       |  1842000
Andre Drummond           | Detroit Pistons        |      0 | C        |  22 | 6-11   | 126.5306 | Connecticut           |  3272091
Kevin Seraphin           | New York Knicks        |      1 | C        |  26 | 6-10   | 126.0771 |                       |  2814000
Brook Lopez              | Brooklyn Nets          |     11 | C        |  28 | 7-0    | 124.7166 | Stanford              | 19689000
Jahlil Okafor            | Philadelphia 76ers     |      8 | C        |  20 | 6-11   | 124.7166 | Duke                  |  4582680
Cristiano Felicio        | Chicago Bulls          |      6 | PF       |  23 | 6-10   | 124.7166 |                       |   525093
[...]

Now, if we’re happy with the results, we can convert the staged foreign table to a standard table

Converting an foreign table to a standard database table

CREATE TABLE AS can be used to materialize a foreign table into a regular table.

Tip

If you intend to use the table multiple times, convert the foreign table to a standard table.

t=> CREATE TABLE real_nba AS
.    SELECT name, team, number, position, age, height, (weight / 2.205) as weight, college, salary
.            FROM nba
.            ORDER BY weight;
executed
t=> SELECT * FROM real_nba LIMIT 5;

name             | team                   | number | position | age | height | weight   | college     | salary
-----------------+------------------------+--------+----------+-----+--------+----------+-------------+---------
Nikola Pekovic   | Minnesota Timberwolves |     14 | C        |  30 | 6-11   |  139.229 |             | 12100000
Boban Marjanovic | San Antonio Spurs      |     40 | C        |  27 | 7-3    | 131.5193 |             |  1200000
Al Jefferson     | Charlotte Hornets      |     25 | C        |  31 | 6-10   | 131.0658 |             | 13500000
Jusuf Nurkic     | Denver Nuggets         |     23 | C        |  21 | 7-0    | 126.9841 |             |  1842000
Andre Drummond   | Detroit Pistons        |      0 | C        |  22 | 6-11   | 126.5306 | Connecticut |  3272091

Error Handling and Limitations

  • Error handling in foreign tables is limited. Any error that occurs during source data parsing will result in the statement aborting.

  • Foreign tables are logical and do not contain any data, their structure is not verified or enforced until a query uses the table. For example, a CSV with the wrong delimiter may cause a query to fail, even though the table has been created successfully:

    t=> SELECT * FROM nba;
    master=> select * from nba;
    Record delimiter mismatch during CSV parsing. User defined line delimiter \n does not match the first delimiter \r\n found in s3://sqream-demo-data/nba.csv
    
  • Since the data for a foreign table is not stored in SQream DB, it can be changed or removed at any time by an external process. As a result, the same query can return different results each time it runs against a foreign table. Similarly, a query might fail if the external data is moved, removed, or has changed structure.