Connect Airbyte to ClickHouse
Airbyte is an open-source data integration platform. It allows the creation of ELT data pipelines and is shipped with more than 140 out-of-the-box connectors. This step-by-step tutorial shows how to connect Airbyte to ClickHouse as a destination and load a sample dataset.1. Download and run Airbyte
Airbyte runs on Docker and uses
docker-compose. Make sure to download and install the latest versions of Docker.Deploy Airbyte by cloning the official Github repository and running
docker-compose upin your favorite terminal:git clone https://github.com/airbytehq/airbyte.git
cd airbyte
docker-compose uptip
The
docker-composecommand may bedocker compose(space, not-) on recent versions of Docker.Once you see the Airbyte banner in your terminal, you can connect to localhost:8000
note
Alternatively, you can signup and use Airbyte Cloud
2. Add ClickHouse as a destination
In this section, we will display how to add a ClickHouse instance as a destination.
Start your ClickHouse server (Airbyte is compatible with ClickHouse version
21.8.10.19or above) or login to your ClickHouse cloud account:clickhouse-server startWithin Airbyte, select the "Destinations" page and add a new destination:
Select ClickHouse from the "Destination type" drop-down list, and Fill out the "Set up the destination" form by providing your ClickHouse hostname and ports, database name, username and password and select if it's an SSL connection (equivalent to the
--secureflag in theclickhouse-client):
Congratulations! you have now added ClickHouse as a destination in Airbyte.
note
In order to use ClickHouse as a destination, the user you'll use need to have the permissions to create databases, tables and insert rows. We recommend creating a dedicated user for Airbyte (eg. my_airbyte_user) with the following permissions:
CREATE USER 'my_airbyte_user'@'%' IDENTIFIED BY 'your_password_here';
GRANT CREATE ON * TO my_airbyte_user;
3. Add a dataset as a source
The example dataset we will use is the New York City Taxi Data (on Github). For this tutorial, we will use a subset of this dataset which corresponds to the month of Jan 2022.
Within Airbyte, select the "Sources" page and add a new source of type file.
Fill out the "Set up the source" form by naming the source and providing the URL of the NYC Taxi Jan 2022 file (see below). Make sure to pick
parquetas file format,HTTPS Public Webas Storage Provider andnyc_taxi_2022as Dataset Name.https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet
Congratulations! You have now added a source file in Airbyte.
4. Create a connection and load the dataset into ClickHouse
Within Airbyte, select the "Connections" page and add a new connection
Select "Use existing source" and select the New York City Taxi Data, the select "Use existing destination" and select you ClickHouse instance.
Fill out the "Set up the connection" form by choosing a Replication Frequency (we will use
manualfor this tutorial) and selectnyc_taxi_2022as the stream you want to sync. Make sure you pickNormalized Tabular Dataas a Normalization.
Now that the connection is created, click on "Sync now" to trigger the data loading (since we picked
Manualas a Replication Frequency)
Your data will start loading, you can expand the view to see Airbyte logs and progress. Once the operation finishes, you'll see a
Completed successfullymessage in the logs:
Connect to your ClickHouse instance using your preferred SQL Client and check the resulting table:
SELECT *
FROM nyc_taxi_2022
LIMIT 10The response should look like:
Query id: 4f79c106-fe49-4145-8eba-15e1cb36d325
┌─extra─┬─mta_tax─┬─VendorID─┬─RatecodeID─┬─tip_amount─┬─airport_fee─┬─fare_amount─┬─DOLocationID─┬─PULocationID─┬─payment_type─┬─tolls_amount─┬─total_amount─┬─trip_distance─┬─passenger_count─┬─store_and_fwd_flag─┬─congestion_surcharge─┬─tpep_pickup_datetime─┬─improvement_surcharge─┬─tpep_dropoff_datetime─┬─_airbyte_ab_id───────────────────────┬─────_airbyte_emitted_at─┬─_airbyte_normalized_at─┬─_airbyte_nyc_taxi_2022_hashid────┐
│ 0 │ 0.5 │ 2 │ 1 │ 2.03 │ 0 │ 17 │ 41 │ 162 │ 1 │ 0 │ 22.33 │ 4.25 │ 3 │ N │ 2.5 │ 2022-01-24T16:02:27 │ 0.3 │ 2022-01-24T16:22:23 │ 000022a5-3f14-4217-9938-5657f9041c8a │ 2022-07-19 04:35:31.000 │ 2022-07-19 04:39:20 │ 91F83E2A3AF3CA79E27BD5019FA7EC94 │
│ 3 │ 0.5 │ 1 │ 1 │ 1.75 │ 0 │ 5 │ 186 │ 246 │ 1 │ 0 │ 10.55 │ 0.9 │ 1 │ N │ 2.5 │ 2022-01-22T23:23:05 │ 0.3 │ 2022-01-22T23:27:03 │ 000036b6-1c6a-493b-b585-4713e433b9cd │ 2022-07-19 04:34:53.000 │ 2022-07-19 04:39:20 │ 5522F328014A7234E23F9FC5FA78FA66 │
│ 0 │ 0.5 │ 2 │ 1 │ 7.62 │ 1.25 │ 27 │ 238 │ 70 │ 1 │ 6.55 │ 45.72 │ 9.16 │ 1 │ N │ 2.5 │ 2022-01-22T19:20:37 │ 0.3 │ 2022-01-22T19:40:51 │ 00003c6d-78ad-4288-a79d-00a62d3ca3c5 │ 2022-07-19 04:34:46.000 │ 2022-07-19 04:39:20 │ 449743975782E613109CEE448AFA0AB3 │
│ 0.5 │ 0.5 │ 2 │ 1 │ 0 │ 0 │ 9.5 │ 234 │ 249 │ 1 │ 0 │ 13.3 │ 1.5 │ 1 │ N │ 2.5 │ 2022-01-22T20:13:39 │ 0.3 │ 2022-01-22T20:26:40 │ 000042f6-6f61-498b-85b9-989eaf8b264b │ 2022-07-19 04:34:47.000 │ 2022-07-19 04:39:20 │ 01771AF57922D1279096E5FFE1BD104A │
│ 0 │ 0 │ 2 │ 5 │ 5 │ 0 │ 60 │ 265 │ 90 │ 1 │ 0 │ 65.3 │ 5.59 │ 1 │ N │ 0 │ 2022-01-25T09:28:36 │ 0.3 │ 2022-01-25T09:47:16 │ 00004c25-53a4-4cd4-b012-a34dbc128aeb │ 2022-07-19 04:35:46.000 │ 2022-07-19 04:39:20 │ CDA4831B683D10A7770EB492CC772029 │
│ 0 │ 0.5 │ 2 │ 1 │ 0 │ 0 │ 11.5 │ 68 │ 170 │ 2 │ 0 │ 14.8 │ 2.2 │ 1 │ N │ 2.5 │ 2022-01-25T13:19:26 │ 0.3 │ 2022-01-25T13:36:19 │ 00005c75-c3c8-440c-a8e8-b1bd2b7b7425 │ 2022-07-19 04:35:52.000 │ 2022-07-19 04:39:20 │ 24D75D8AADD488840D78EA658EBDFB41 │
│ 2.5 │ 0.5 │ 1 │ 1 │ 0.88 │ 0 │ 5.5 │ 79 │ 137 │ 1 │ 0 │ 9.68 │ 1.1 │ 1 │ N │ 2.5 │ 2022-01-22T15:45:09 │ 0.3 │ 2022-01-22T15:50:16 │ 0000acc3-e64f-4b58-8e15-dc47ff1685f3 │ 2022-07-19 04:34:37.000 │ 2022-07-19 04:39:20 │ 2BB5B8E849A438E08F7FCF789E7D7E65 │
│ 1.75 │ 0.5 │ 1 │ 1 │ 7.5 │ 1.25 │ 27.5 │ 17 │ 138 │ 1 │ 0 │ 37.55 │ 9 │ 1 │ N │ 0 │ 2022-01-30T21:58:19 │ 0.3 │ 2022-01-30T22:19:30 │ 0000b339-b44b-40b0-99f8-ebbf2092cc5b │ 2022-07-19 04:38:10.000 │ 2022-07-19 04:39:20 │ DCCE79199EF9217CD769EFD5271302FE │
│ 0.5 │ 0.5 │ 2 │ 1 │ 0 │ 0 │ 13 │ 79 │ 140 │ 2 │ 0 │ 16.8 │ 3.19 │ 1 │ N │ 2.5 │ 2022-01-26T20:43:14 │ 0.3 │ 2022-01-26T20:58:08 │ 0000caa8-d46a-4682-bd25-38b2b0b9300b │ 2022-07-19 04:36:36.000 │ 2022-07-19 04:39:20 │ F502BE51809AF36582561B2D037B4DDC │
│ 0 │ 0.5 │ 2 │ 1 │ 1.76 │ 0 │ 5.5 │ 141 │ 237 │ 1 │ 0 │ 10.56 │ 0.72 │ 2 │ N │ 2.5 │ 2022-01-27T15:19:54 │ 0.3 │ 2022-01-27T15:26:23 │ 0000cd63-c71f-4eb9-9c27-09f402fddc76 │ 2022-07-19 04:36:55.000 │ 2022-07-19 04:39:20 │ 8612CDB63E13D70C1D8B34351A7CA00D │
└───────┴─────────┴──────────┴────────────┴────────────┴─────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴───────────────┴─────────────────┴────────────────────┴──────────────────────┴──────────────────────┴───────────────────────┴───────────────────────┴──────────────────────────────────────┴─────────────────────────┴────────────────────────┴──────────────────────────────────┘SELECT count(*)
FROM nyc_taxi_2022The response is:
Query id: a9172d39-50f7-421e-8330-296de0baa67e
┌─count()─┐
│ 2392428 │
└─────────┘
Notice that Airbyte automatically inferred the data types and added 4 columns to the destination table. These columns are used by Airbyte to manage the replication logic and log the operations. More details are available in the Airbyte official documentation.
`_airbyte_ab_id` String,
`_airbyte_emitted_at` DateTime64(3, 'GMT'),
`_airbyte_normalized_at` DateTime,
`_airbyte_nyc_taxi_072021_hashid` StringNow that the dataset is loaded on your ClickHouse instance, you can create an new table and use more suitable ClickHouse data types (more details).
- Congratulations - you have successfully loaded the NYC taxi data into ClickHouse using Airbyte!