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Quickwit static binary packages are also provided as `musl` builds. These packages don't require you to install any external library and can be automatically picked during installation on your system if the required libc version is not present. You can also download and manually install a static binary package.
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Quickwit static binary packages are also provided as `musl` builds. These packages don't require you to install any external library and can be automatically picked during installation on your system if the required libc version is not present. You can also download and manually install a static binary package.
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@@ -65,14 +65,14 @@ quickwit-{version}
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-`config/quickwit.yaml`: is the default configuration file.
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-`LICENSE_AGPLv3.0.txt`: the license file.
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-`quickwit`: the quickwit executable binary.
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-`qwdata/`: the default data directory.
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-`qwdata/`: the default data directory.
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## Use the docker image
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If you use docker, this might be one of the quickest way to get going.
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If you use docker, this might be one of the quickest way to get going.
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The following command will pull the image from [dockerhub](https://hub.docker.com/r/quickwit/quickwit)
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and gets you right in the shell of the running container ready to execute Quickwit commands.
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and gets you right in the shell of the running container ready to execute Quickwit commands.
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Note that we are also mounting the working directory as volume. This is useful when you already have your dataset ready on your machine and want to work with Quickwit docker image.
Copy file name to clipboardExpand all lines: docs/guides/add-full-text-search-to-your-olap-db.md
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This guide will help you add full-text search to a well-known OLAP database, Clickhouse, using the Quickwit search streaming feature. Indeed Quickwit exposes a REST endpoint that streams ids or whatever attributes matching a search query **extremely fast** (up to 50 million in 1 second), and Clickhouse can easily use them with joins queries.
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This guide will help you add full-text search to a well-known OLAP database, Clickhouse, using the Quickwit search streaming feature. Indeed Quickwit exposes a REST endpoint that streams ids or whatever attributes matching a search query **extremely fast** (up to 50 million in 1 second), and Clickhouse can easily use them with joins queries.
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We will take the [Github archive dataset](https://www.gharchive.org/), which gathers more than 3 billion Github events: `WatchEvent`, `PullRequestEvent`, `IssuesEvent`... You can dive into this [great analysis](https://ghe.clickhouse.tech/) made by Clickhouse to have a good understanding of the dataset. We also took strong inspiration from this work, and we are very grateful to them for sharing this.
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## Start a searcher
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```bash
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./quickwit service run searcher
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./quickwit run --service searcher
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```
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This command will start an HTTP server with a [REST API](../reference/rest-api.md). We are now
Clickhouse has an exciting feature called [URL Table Engine](https://clickhouse.com/docs/en/engines/table-engines/special/url/) that queries data from a remote HTTP/HTTPS server.
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This is precisely what we need: by creating a table pointing to Quickwit search stream endpoint, we will fetch ids that match a query from Clickhouse.
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This is precisely what we need: by creating a table pointing to Quickwit search stream endpoint, we will fetch ids that match a query from Clickhouse.
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```SQL
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SELECTcount(*) FROM url('http://127.0.0.1:7280/api/v1/gh-archive/search/stream?query=log4j+OR+log4shell&fastField=id&outputFormat=clickHouseRowBinary', RowBinary, 'id UInt64')
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