Serverless SQL

Learn how serverless SQL enables you to perform analysis

TileDB natively supports tabular data and enables querying any dataset, even non-tabular modalities with SQL. TileDB offers serverless SQL capabilities, which enable scalable and elastic SQL that is performant and cost-efficient.

SQL execution

TileDB offers several ways to interact and run serverless SQL queries. The first and quickest way is to use the tiledb.cloud.sql.exec command, which directly executes SQL and returns it as a dataframe.

Python

tiledb.cloud.sql.exec(
    f"SELECT * FROM `{table_uri}` WHERE tpep_pickup_datetime BETWEEN '2022-01-01 00:00:00' and '2022-01-01 00:01:00'"
)

	tpep_pickup_datetime	PULocationID	congestion_surcharge	improvement_surcharge	VendorID	payment_type	tpep_dropoff_datetime	DOLocationID	mta_tax	passenger_count	trip_distance	RatecodeID	store_and_fwd_flag	total_amount	fare_amount	extra	tip_amount	tolls_amount
0	2022-01-01 00:00:08+00:00	138	0.0	0.3	2.0	1.0	2022-01-01 00:14:14+00:00	255	0.5	1.0	7.94	1.0	N	30.41	23.0	0.50	4.86	0.00
1	2022-01-01 00:00:08+00:00	138	0.0	0.3	2.0	1.0	2022-01-01 00:14:14+00:00	255	0.5	1.0	7.94	1.0	N	30.41	23.0	0.50	4.86	0.00
2	2022-01-01 00:00:11+00:00	132	0.0	0.3	2.0	2.0	2022-01-01 00:14:29+00:00	215	0.5	1.0	5.55	1.0	N	20.05	17.5	0.50	0.00	0.00
3	2022-01-01 00:00:11+00:00	132	0.0	0.3	2.0	2.0	2022-01-01 00:14:29+00:00	215	0.5	1.0	5.55	1.0	N	20.05	17.5	0.50	0.00	0.00
4	2022-01-01 00:00:17+00:00	170	2.5	0.3	2.0	1.0	2022-01-01 00:31:04+00:00	68	0.5	1.0	3.86	1.0	N	28.56	20.0	0.50	4.76	0.00
5	2022-01-01 00:00:17+00:00	170	2.5	0.3	2.0	1.0	2022-01-01 00:31:04+00:00	68	0.5	1.0	3.86	1.0	N	28.56	20.0	0.50	4.76	0.00
6	2022-01-01 00:00:18+00:00	79	2.5	0.3	2.0	1.0	2022-01-01 00:13:27+00:00	209	0.5	1.0	3.37	1.0	N	20.16	13.0	0.50	3.36	0.00
7	2022-01-01 00:00:18+00:00	79	2.5	0.3	2.0	1.0	2022-01-01 00:13:27+00:00	209	0.5	1.0	3.37	1.0	N	20.16	13.0	0.50	3.36	0.00
8	2022-01-01 00:00:20+00:00	68	2.5	0.3	2.0	1.0	2022-01-01 00:20:51+00:00	256	0.5	1.0	4.89	1.0	N	28.34	18.0	0.50	6.54	0.00
9	2022-01-01 00:00:20+00:00	68	2.5	0.3	2.0	1.0	2022-01-01 00:20:51+00:00	256	0.5	1.0	4.89	1.0	N	28.34	18.0	0.50	6.54	0.00
10	2022-01-01 00:00:23+00:00	132	2.5	0.3	1.0	1.0	2022-01-01 00:24:06+00:00	236	0.5	1.0	19.10	2.0	N	75.72	52.0	3.75	12.62	6.55
11	2022-01-01 00:00:23+00:00	132	2.5	0.3	1.0	1.0	2022-01-01 00:24:06+00:00	236	0.5	1.0	19.10	2.0	N	75.72	52.0	3.75	12.62	6.55
12	2022-01-01 00:00:25+00:00	132	0.0	0.3	2.0	1.0	2022-01-01 00:22:31+00:00	65	0.5	3.0	18.30	1.0	N	63.19	48.0	0.50	12.64	0.00
13	2022-01-01 00:00:25+00:00	132	0.0	0.3	2.0	1.0	2022-01-01 00:22:31+00:00	65	0.5	3.0	18.30	1.0	N	63.19	48.0	0.50	12.64	0.00
14	2022-01-01 00:00:27+00:00	211	2.5	0.3	2.0	1.0	2022-01-01 00:10:14+00:00	209	0.5	6.0	3.90	1.0	N	21.19	12.5	0.50	4.89	0.00
15	2022-01-01 00:00:27+00:00	211	2.5	0.3	2.0	1.0	2022-01-01 00:10:14+00:00	209	0.5	6.0	3.90	1.0	N	21.19	12.5	0.50	4.89	0.00
16	2022-01-01 00:00:29+00:00	238	2.5	0.3	2.0	2.0	2022-01-01 00:06:52+00:00	263	0.5	2.0	1.46	1.0	N	10.80	7.0	0.50	0.00	0.00
17	2022-01-01 00:00:29+00:00	238	2.5	0.3	2.0	2.0	2022-01-01 00:06:52+00:00	263	0.5	2.0	1.46	1.0	N	10.80	7.0	0.50	0.00	0.00
18	2022-01-01 00:00:30+00:00	132	0.0	0.3	2.0	2.0	2022-01-01 00:10:18+00:00	216	0.5	1.0	5.05	1.0	N	18.05	15.5	0.50	0.00	0.00
19	2022-01-01 00:00:30+00:00	132	0.0	0.3	2.0	2.0	2022-01-01 00:10:18+00:00	216	0.5	1.0	5.05	1.0	N	18.05	15.5	0.50	0.00	0.00
20	2022-01-01 00:00:30+00:00	170	2.5	0.3	2.0	1.0	2022-01-01 00:20:31+00:00	166	0.5	1.0	5.05	1.0	N	27.36	19.0	0.50	4.56	0.00
21	2022-01-01 00:00:30+00:00	170	2.5	0.3	2.0	1.0	2022-01-01 00:20:31+00:00	166	0.5	1.0	5.05	1.0	N	27.36	19.0	0.50	4.56	0.00
22	2022-01-01 00:00:44+00:00	7	0.0	0.3	1.0	2.0	2022-01-01 00:05:29+00:00	7	0.5	1.0	0.20	1.0	N	6.30	5.0	0.50	0.00	0.00
23	2022-01-01 00:00:44+00:00	7	0.0	0.3	1.0	2.0	2022-01-01 00:05:29+00:00	7	0.5	1.0	0.20	1.0	N	6.30	5.0	0.50	0.00	0.00
24	2022-01-01 00:00:44+00:00	263	2.5	0.3	2.0	1.0	2022-01-01 00:08:54+00:00	233	0.5	1.0	2.17	1.0	N	14.76	8.5	0.50	2.46	0.00
25	2022-01-01 00:00:44+00:00	263	2.5	0.3	2.0	1.0	2022-01-01 00:08:54+00:00	233	0.5	1.0	2.17	1.0	N	14.76	8.5	0.50	2.46	0.00
26	2022-01-01 00:00:46+00:00	132	0.0	0.3	2.0	1.0	2022-01-01 00:28:44+00:00	33	0.5	3.0	19.55	1.0	N	63.31	52.5	0.50	8.26	0.00
27	2022-01-01 00:00:46+00:00	132	0.0	0.3	2.0	1.0	2022-01-01 00:28:44+00:00	33	0.5	3.0	19.55	1.0	N	63.31	52.5	0.50	8.26	0.00
28	2022-01-01 00:00:51+00:00	79	2.5	0.3	2.0	2.0	2022-01-01 00:07:30+00:00	233	0.5	6.0	2.24	1.0	N	11.80	8.0	0.50	0.00	0.00
29	2022-01-01 00:00:51+00:00	79	2.5	0.3	2.0	2.0	2022-01-01 00:07:30+00:00	233	0.5	6.0	2.24	1.0	N	11.80	8.0	0.50	0.00	0.00
30	2022-01-01 00:00:52+00:00	236	2.5	0.3	1.0	1.0	2022-01-01 00:09:51+00:00	237	0.5	2.0	1.40	1.0	N	15.35	8.5	3.00	3.05	0.00
31	2022-01-01 00:00:52+00:00	236	2.5	0.3	1.0	1.0	2022-01-01 00:09:51+00:00	237	0.5	2.0	1.40	1.0	N	15.35	8.5	3.00	3.05	0.00
32	2022-01-01 00:00:54+00:00	152	2.5	0.3	2.0	1.0	2022-01-01 00:11:33+00:00	48	0.5	4.0	4.51	1.0	N	23.16	15.5	0.50	3.86	0.00
33	2022-01-01 00:00:54+00:00	152	2.5	0.3	2.0	1.0	2022-01-01 00:11:33+00:00	48	0.5	4.0	4.51	1.0	N	23.16	15.5	0.50	3.86	0.00

Task graphs

Serverless SQL can be used with TileDB task graphs to enable large-scale, distributed computations. See SQL and arrays in Task Graphs for examples and details.

Python DBI

TileDB also offers Python DBI and Java JDBC connectors, which allow for integration of TileDB serverless SQL into existing tools and applications.