Foreign Tables¶
Foreign tables can be used to run queries directly on data without inserting it into BLUE first. BLUE 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.
Although foreign tables can be used without inserting data into BLUE, 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.
Supported Data Formats¶
BLUE supports foreign tables over:
Text files (e.g. CSV, PSV, TSV)
ORC
Parquet
JSON
Avro
Supported Data Staging¶
SQream can stage data from:
Using Foreign Tables¶
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 interact with a CSV file.
The file is stored on Amazon Web Services, at s3://sqream-demo-data/nba_players.csv.
We will make note of the file structure, to create a matching CREATE_EXTERNAL_TABLE statement.
Creating a Foreign Table¶
Based on the source file structure, 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
)
WRAPPER csv_fdw -- Text file
OPTIONS
(
LOCATION = '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 Amazon Web Services object.
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:
SELECT *
FROM
nba
LIMIT 10;
Output:
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 content and modify it before loading. 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:
SELECT
name,
team,
number,
position,
age,
height, (weight / 2.205) as weight,
college, salary
FROM nba
ORDER BY weight;
Output:
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 a 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.
CREATE TABLE real_nba AS
SELECT
name,
team,
number,
position,
age,
height,
(weight / 2.205) AS weight,
college, salary
FROM nba
ORDER BY weight;
SELECT * FROM real_nba LIMIT 5;
Output:
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:
SELECT * FROM nba;
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 BLUE, it can be changed or removed at any time by a foreign 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 foreign data is moved, removed, or has changed structure.