Query External Data
Query on a file
Currently, we support queries on Parquet, CSV, ORC, and NDJson format file(s).
We use the Taxi Zone Lookup Table data as an example.
bash
curl "https://d37ci6vzurychx.cloudfront.net/misc/taxi+_zone_lookup.csv" -o /tmp/taxi+_zone_lookup.csvcurl "https://d37ci6vzurychx.cloudfront.net/misc/taxi+_zone_lookup.csv" -o /tmp/taxi+_zone_lookup.csvCreate an external table:
sql
CREATE EXTERNAL TABLE taxi_zone_lookup with (location='/tmp/taxi+_zone_lookup.csv',format='csv');CREATE EXTERNAL TABLE taxi_zone_lookup with (location='/tmp/taxi+_zone_lookup.csv',format='csv');You can check the schema of the external table like follows:
sql
DESC TABLE taxi_zone_lookup;DESC TABLE taxi_zone_lookup;sql
+--------------------+----------------------+------+------+--------------------------+---------------+
| Column | Type | Key | Null | Default | Semantic Type |
+--------------------+----------------------+------+------+--------------------------+---------------+
| LocationID | Int64 | | YES | | FIELD |
| Borough | String | | YES | | FIELD |
| Zone | String | | YES | | FIELD |
| service_zone | String | | YES | | FIELD |
| greptime_timestamp | TimestampMillisecond | PRI | NO | 1970-01-01 00:00:00+0000 | TIMESTAMP |
+--------------------+----------------------+------+------+--------------------------+---------------+
4 rows in set (0.00 sec)+--------------------+----------------------+------+------+--------------------------+---------------+
| Column | Type | Key | Null | Default | Semantic Type |
+--------------------+----------------------+------+------+--------------------------+---------------+
| LocationID | Int64 | | YES | | FIELD |
| Borough | String | | YES | | FIELD |
| Zone | String | | YES | | FIELD |
| service_zone | String | | YES | | FIELD |
| greptime_timestamp | TimestampMillisecond | PRI | NO | 1970-01-01 00:00:00+0000 | TIMESTAMP |
+--------------------+----------------------+------+------+--------------------------+---------------+
4 rows in set (0.00 sec)Note
Here, you may notice there is a greptime_timestamp column, which doesn't exist in the file. This is because when creating an external table, if we didn't specify a TIME INDEX column, the greptime_timestamp column is automatically added as the TIME INDEX column with a default value of 1970-01-01 00:00:00+0000. You can find more details in the create document.
Now you can query on the external table:
sql
SELECT `Zone`, `Borough` FROM taxi_zone_lookup LIMIT 5;SELECT `Zone`, `Borough` FROM taxi_zone_lookup LIMIT 5;sql
+-------------------------+---------------+
| Zone | Borough |
+-------------------------+---------------+
| Newark Airport | EWR |
| Jamaica Bay | Queens |
| Allerton/Pelham Gardens | Bronx |
| Alphabet City | Manhattan |
| Arden Heights | Staten Island |
+-------------------------+---------------++-------------------------+---------------+
| Zone | Borough |
+-------------------------+---------------+
| Newark Airport | EWR |
| Jamaica Bay | Queens |
| Allerton/Pelham Gardens | Bronx |
| Alphabet City | Manhattan |
| Arden Heights | Staten Island |
+-------------------------+---------------+Query on a directory
Let's download some data:
bash
mkdir /tmp/external
curl "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet" -o /tmp/external/yellow_tripdata_2022-01.parquet
curl "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-02.parquet" -o /tmp/external/yellow_tripdata_2022-02.parquetmkdir /tmp/external
curl "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet" -o /tmp/external/yellow_tripdata_2022-01.parquet
curl "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-02.parquet" -o /tmp/external/yellow_tripdata_2022-02.parquetVerify the download:
bash
ls -l /tmp/external
total 165368
-rw-r--r-- 1 wenyxu wheel 38139949 Apr 28 14:35 yellow_tripdata_2022-01.parquet
-rw-r--r-- 1 wenyxu wheel 45616512 Apr 28 14:36 yellow_tripdata_2022-02.parquetls -l /tmp/external
total 165368
-rw-r--r-- 1 wenyxu wheel 38139949 Apr 28 14:35 yellow_tripdata_2022-01.parquet
-rw-r--r-- 1 wenyxu wheel 45616512 Apr 28 14:36 yellow_tripdata_2022-02.parquetCreate the external table:
sql
CREATE EXTERNAL TABLE yellow_tripdata with(location='/tmp/external/',format='parquet');CREATE EXTERNAL TABLE yellow_tripdata with(location='/tmp/external/',format='parquet');Run queries:
sql
SELECT count(*) FROM yellow_tripdata;SELECT count(*) FROM yellow_tripdata;sql
+-----------------+
| COUNT(UInt8(1)) |
+-----------------+
| 5443362 |
+-----------------+
1 row in set (0.48 sec)+-----------------+
| COUNT(UInt8(1)) |
+-----------------+
| 5443362 |
+-----------------+
1 row in set (0.48 sec)sql
SELECT * FROM yellow_tripdata LIMIT 5;SELECT * FROM yellow_tripdata LIMIT 5;sql
+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
| VendorID | tpep_pickup_datetime | tpep_dropoff_datetime | passenger_count | trip_distance | RatecodeID | store_and_fwd_flag | PULocationID | DOLocationID | payment_type | fare_amount | extra | mta_tax | tip_amount | tolls_amount | improvement_surcharge | total_amount | congestion_surcharge | airport_fee | greptime_timestamp |
+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
| 1 | 2022-02-01 00:06:58 | 2022-02-01 00:19:24 | 1 | 5.4 | 1 | N | 138 | 252 | 1 | 17 | 1.75 | 0.5 | 3.9 | 0 | 0.3 | 23.45 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 1 | 2022-02-01 00:38:22 | 2022-02-01 00:55:55 | 1 | 6.4 | 1 | N | 138 | 41 | 2 | 21 | 1.75 | 0.5 | 0 | 6.55 | 0.3 | 30.1 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 1 | 2022-02-01 00:03:20 | 2022-02-01 00:26:59 | 1 | 12.5 | 1 | N | 138 | 200 | 2 | 35.5 | 1.75 | 0.5 | 0 | 6.55 | 0.3 | 44.6 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 2 | 2022-02-01 00:08:00 | 2022-02-01 00:28:05 | 1 | 9.88 | 1 | N | 239 | 200 | 2 | 28 | 0.5 | 0.5 | 0 | 3 | 0.3 | 34.8 | 2.5 | 0 | 1970-01-01 00:00:00 |
| 2 | 2022-02-01 00:06:48 | 2022-02-01 00:33:07 | 1 | 12.16 | 1 | N | 138 | 125 | 1 | 35.5 | 0.5 | 0.5 | 8.11 | 0 | 0.3 | 48.66 | 2.5 | 1.25 | 1970-01-01 00:00:00 |
+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
5 rows in set (0.11 sec)+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
| VendorID | tpep_pickup_datetime | tpep_dropoff_datetime | passenger_count | trip_distance | RatecodeID | store_and_fwd_flag | PULocationID | DOLocationID | payment_type | fare_amount | extra | mta_tax | tip_amount | tolls_amount | improvement_surcharge | total_amount | congestion_surcharge | airport_fee | greptime_timestamp |
+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
| 1 | 2022-02-01 00:06:58 | 2022-02-01 00:19:24 | 1 | 5.4 | 1 | N | 138 | 252 | 1 | 17 | 1.75 | 0.5 | 3.9 | 0 | 0.3 | 23.45 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 1 | 2022-02-01 00:38:22 | 2022-02-01 00:55:55 | 1 | 6.4 | 1 | N | 138 | 41 | 2 | 21 | 1.75 | 0.5 | 0 | 6.55 | 0.3 | 30.1 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 1 | 2022-02-01 00:03:20 | 2022-02-01 00:26:59 | 1 | 12.5 | 1 | N | 138 | 200 | 2 | 35.5 | 1.75 | 0.5 | 0 | 6.55 | 0.3 | 44.6 | 0 | 1.25 | 1970-01-01 00:00:00 |
| 2 | 2022-02-01 00:08:00 | 2022-02-01 00:28:05 | 1 | 9.88 | 1 | N | 239 | 200 | 2 | 28 | 0.5 | 0.5 | 0 | 3 | 0.3 | 34.8 | 2.5 | 0 | 1970-01-01 00:00:00 |
| 2 | 2022-02-01 00:06:48 | 2022-02-01 00:33:07 | 1 | 12.16 | 1 | N | 138 | 125 | 1 | 35.5 | 0.5 | 0.5 | 8.11 | 0 | 0.3 | 48.66 | 2.5 | 1.25 | 1970-01-01 00:00:00 |
+----------+----------------------+-----------------------+-----------------+---------------+------------+--------------------+--------------+--------------+--------------+-------------+-------+---------+------------+--------------+-----------------------+--------------+----------------------+-------------+---------------------+
5 rows in set (0.11 sec)Note
The query result includes the value of the greptime_timestamp column, although it does not exist in the original file. All these column values are 1970-01-01 00:00:00+0000, because when we create an external table, the greptime_timestamp column is automatically added with a default value of 1970-01-01 00:00:00+0000. You can find more details in the create document.