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Full-text search with ClickHouse and Quickwit

If you are using ClickHouse and you end up in a situation wishing you had full text search abilities you have to know that it is possible to use a search engine within ClickHouse. Quickwit is a distributed search engine. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets and the good news is that Quickwit can be used within ClickHouse. In this guide we are going to show you how you can add full text search to ClickHouse.

Installing ClickHouse

The first step is to install Quickwit and ClickHouse if you don’t have them installed already, follow the instruction below to install ClickHouse:

sudo apt-get install apt-transport-https ca-certificates dirmngr
sudo apt-key adv --keyserver hkp:// --recv E0C56BD4

echo "deb main/" | sudo tee \
sudo apt-get update

sudo apt-get install -y clickhouse-server clickhouse-client

sudo service clickhouse-server start

Installing Quickwit

Quickwit is relying on two external libraries to work correctly. You will need to install them before installing Quickwit

# Quickwit depends on the following external libraries to work correctly
sudo apt-get -y update
sudo apt-get -y install libpq-dev libssl-dev

Once these two libraries are installed you can go ahead and install Quickwit:

curl -L | sh
# Quickwit detects the config from CLI args or the QW_CONFIG env variable.
# Let's set QW_CONFIG to the default config.
cd quickwit-v*/
export QW_CONFIG=./config/quickwit.yaml

You can test that Quickwit has been properly installed by running the following command:

./quickwit --version

Now that both ClickHouse and Quickwit are installed and run all we have to do is add some data to both of them.

Indexing Data in QuickWit

The first thing we need to do is provide a data schema for the data we are going to use. We are going to use a subset of the data provided by GitHub. You can find the original data here, the dataset we are going to use has slightly been modified in order to be more practical to use.

curl -o gh-archive-index-config.yaml
./quickwit index create --index-config gh-archive-index-config.yaml

Now that the data schema is defined, let’s download and index some data in Quickwit:

gunzip -c gh-archive-2021-12-text-only.json.gz | ./quickwit index ingest --index gh-archive

You can search through your data within Quickwit:

./quickwit index search --index gh-archive --query "clickhouse"

But we want to use it in conjunction with ClickHouse, so in order to do so, we will need to create a searcher.

./quickwit run --service searcher

This command will start an HTTP server with a REST API. We are now ready to fetch some ids with the search stream endpoint. Let's start by streaming them on a simple query and with a CSV output format.

curl ""

In the remaining of this guide we will be using the ClickHouse binary output format to speed up queries using ClickHouse.

Adding Data to ClickHouse

First thing first, we need to connect to the ClickHouse database. Let’s use the clickhouse-client to do it.

clickhouse-client –password <PASSWORD>

The first thing we need to do is to create a database:

USE github;

Now we need to create the table that’s going to store our data:

CREATE TABLE github.github_events
`id` UInt64,
`event_type` Enum('CommitCommentEvent' = 1, 'CreateEvent' = 2, 'DeleteEvent' = 3, 'ForkEvent' = 4, 'GollumEvent' = 5, 'IssueCommentEvent' = 6, 'IssuesEvent' = 7, 'MemberEvent' = 8, 'PublicEvent' = 9, 'PullRequestEvent' = 10, 'PullRequestReviewCommentEvent' = 11, 'PushEvent' = 12, 'ReleaseEvent' = 13, 'SponsorshipEvent' = 14, 'WatchEvent' = 15, 'GistEvent' = 16, 'FollowEvent' = 17, 'DownloadEvent' = 18, 'PullRequestReviewEvent' = 19, 'ForkApplyEvent' = 20, 'Event' = 21, 'TeamAddEvent' = 22),
`actor_login` LowCardinality(String),
`repo_name` LowCardinality(String),
`created_at` Int64,
`action` Enum('none' = 0, 'created' = 1, 'added' = 2, 'edited' = 3, 'deleted' = 4, 'opened' = 5, 'closed' = 6, 'reopened' = 7, 'assigned' = 8, 'unassigned' = 9, 'labeled' = 10, 'unlabeled' = 11, 'review_requested' = 12, 'review_request_removed' = 13, 'synchronize' = 14, 'started' = 15, 'published' = 16, 'update' = 17, 'create' = 18, 'fork' = 19, 'merged' = 20),
`comment_id` UInt64,
`body` String,
`ref` LowCardinality(String),
`number` UInt32,
`title` String,
`labels` Array(LowCardinality(String)),
`additions` UInt32,
`deletions` UInt32,
`commit_id` String
ENGINE = MergeTree
ORDER BY (event_type, repo_name, created_at)

We are going to add some data to ClickHouse. It’s the same dataset as the one we have indexed in Quickwit but this time it does not include text fields since they are indexed already.

gunzip -c gh-archive-2021-12.json.gz | clickhouse-client --query="INSERT INTO github.github_events FORMAT JSONEachRow" --password <PASSWORD>

Now that the data is in ClickHouse we can query them using the clikchouse-client.

count() AS stars
FROM github.github_events
WHERE event_type = 'WatchEvent'
GROUP BY repo_name

Full-text search within ClickHouse

So we have data both in ClickHouse and in QuickWit all there is to do now is to query them! The url function allows you to fetch ids using the Quickwit search stream:

SELECT count(*)
FROM url('', RowBinary, 'id UInt64')

In this query above we are counting the number of ID returned by the ClickHouse query executed in QuickWit. As you can see below, it’s returning the following:

│ 2012 │

1 rows in set. Elapsed: 0.010 sec. Processed 2.01 thousand rows, 16.10 KB (210.35 thousand rows/s., 1.68 MB/s.)

We can search multiple tokens by separating them with a + :

SELECT count(*) FROM url('', RowBinary, 'id UInt64')

In the query above we are searching for documents containing the words ClickHouse AND cloki. Now we can tweak the query around to search for ClickHouse OR cloki:

SELECT count(*)
FROM url('', RowBinary, 'id UInt64')

So the full text search is working, now let’s combine it with some GROUP BY that would be done on the ClickHouse side. Here we want to know how many rows match the words: ClickHouse, cloki or quickwit and in which GitHub repository there located in.

count(*) AS count,
repo_name AS repo
FROM github.github_events
FROM url('', RowBinary, 'id UInt64')

And as you can see below, it is fast:

│ 874 │ ClickHouse/ClickHouse │
│ 112 │ traceon/ClickHouse │
│ 112 │ quickwit-inc/quickwit │
│ 110 │ PostHog/posthog │
│ 73 │ PostHog/charts-clickhouse │
│ 64 │ datafuselabs/databend │
│ 54 │ airbytehq/airbyte │
│ 53 │ ClickHouse/clickhouse-jdbc │
│ 37 │ getsentry/snuba │
│ 37 │ PostHog/ │

│ 1 │ antrea-io/antrea │
│ 1 │ python/typeshed │
│ 1 │ Sunt-ing/database-system-readings │
│ 1 │ duckdb/duckdb │
│ 1 │ open-botech/ClickHouse │

195 rows in set. Elapsed: 0.518 sec. Processed 45.43 million rows, 396.87 MB (87.77 million rows/s., 766.79 MB/s.)

The query is really fast, returning the result in 0.5 second.


Using Quickwit within ClickHouse gives a lot of flexibility in how you work with your data, especially when your data contains textual information and you need to be able to search through them very quickly. You can find more information on how to use Quickwit directly on their documentation.