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 external 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.
Supported Data Formats
SQream DB supports foreign tables over:
Text files (e.g. CSV, PSV, TSV)
ORC
Parquet
Supported Data Staging
SQream can stage data from:
a local filesystem (e.g.
/mnt/storage/....)Inserting Data Using Amazon S3 buckets (e.g.
s3://pp-secret-bucket/users/*.parquet)Using SQream in an HDFS Environment (e.g.
hdfs://hadoop-nn.piedpiper.com/rhendricks/*.csv)
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 want to interact with a CSV file. Here’s a peek at the table contents:
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_EXTERNAL_TABLE statement.
Creating a 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 varchar,
Team varchar,
Number tinyint,
Position varchar,
Age tinyint,
Height varchar,
Weight real,
College varchar,
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 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.
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.