- Blog: [Extracting, Converting, and Querying Data in Local Files using clickhouse-local](https://clickhouse.com/blog/extracting-converting-querying-local-files-with-sql-clickhouse-local)
`clickhouse-local` is an easy-to-use version of ClickHouse that is ideal for developers who need to perform fast processing on local and remote files using SQL without having to install a full database server. With `clickhouse-local`, developers can use SQL commands (using the [ClickHouse SQL dialect](../../sql-reference/index.md)) directly from the command line, providing a simple and efficient way to access ClickHouse features without the need for a full ClickHouse installation. One of the main benefits of `clickhouse-local` is that it is already included when installing [clickhouse-client](https://clickhouse.com/docs/en/integrations/sql-clients/clickhouse-client-local). This means that developers can get started with `clickhouse-local` quickly, without the need for a complex installation process.
While `clickhouse-local` is a great tool for development and testing purposes, and for processing files, it is not suitable for serving end users or applications. In these scenarios, it is recommended to use the open-source [ClickHouse](https://clickhouse.com/docs/en/install). ClickHouse is a powerful OLAP database that is designed to handle large-scale analytical workloads. It provides fast and efficient processing of complex queries on large datasets, making it ideal for use in production environments where high-performance is critical. Additionally, ClickHouse offers a wide range of features such as replication, sharding, and high availability, which are essential for scaling up to handle large datasets and serving applications. If you need to handle larger datasets or serve end users or applications, we recommend using open-source ClickHouse instead of `clickhouse-local`.
Please read the docs below that show example use cases for `clickhouse-local`, such as [querying local CSVs](#query-data-in-a-csv-file-using-sql) or [reading a parquet file in S3](#query-data-in-a-parquet-file-in-aws-s3).
`clickhouse-local` is executed using the same `clickhouse` binary that runs the ClickHouse server and `clickhouse-client`. The easiest way to download the latest version is with the following command:
```bash
curl https://clickhouse.com/ | sh
```
:::note
The binary you just downloaded can run all sorts of ClickHouse tools and utilities. If you want to run ClickHouse as a database server, check out the [Quick Start](../../quick-start.mdx).
:::
## Query data in a CSV file using SQL
A common use of `clickhouse-local` is to run ad-hoc queries on files: where you don't have to insert the data into a table. `clickhouse-local` can stream the data from a file into a temporary table and execute your SQL.
If the file is sitting on the same machine as `clickhouse-local`, use the `file` table engine. The following `reviews.tsv` file contains a sampling of Amazon product reviews:
```bash
./clickhouse local -q "SELECT * FROM file('reviews.tsv')"
```
ClickHouse knows the file uses a tab-separated format from filename extension. If you need to explicitly specify the format, simply add one of the [many ClickHouse input formats](../../interfaces/formats.md):
The `file` table function creates a table, and you can use `DESCRIBE` to see the inferred schema:
```bash
./clickhouse local -q "DESCRIBE file('reviews.tsv')"
```
```response
marketplace Nullable(String)
customer_id Nullable(Int64)
review_id Nullable(String)
product_id Nullable(String)
product_parent Nullable(Int64)
product_title Nullable(String)
product_category Nullable(String)
star_rating Nullable(Int64)
helpful_votes Nullable(Int64)
total_votes Nullable(Int64)
vine Nullable(String)
verified_purchase Nullable(String)
review_headline Nullable(String)
review_body Nullable(String)
review_date Nullable(Date)
```
Let's find a product with the highest rating:
```bash
./clickhouse local -q "SELECT
argMax(product_title,star_rating),
max(star_rating)
FROM file('reviews.tsv')"
```
```response
Monopoly Junior Board Game 5
```
## Query data in a Parquet file in AWS S3
If you have a file in S3, use `clickhouse-local` and the `s3` table function to query the file in place (without inserting the data into a ClickHouse table). We have a file named `house_0.parquet` in a public bucket that contains home prices of property sold in the United Kingdom. Let's see how many rows it has:
```bash
./clickhouse local -q "
SELECT count()
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')"
```
The file has 2.7M rows:
```response
2772030
```
It's always useful to see what the inferred schema that ClickHouse determines from the file:
```bash
./clickhouse local -q "DESCRIBE s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')"
```
```response
price Nullable(Int64)
date Nullable(UInt16)
postcode1 Nullable(String)
postcode2 Nullable(String)
type Nullable(String)
is_new Nullable(UInt8)
duration Nullable(String)
addr1 Nullable(String)
addr2 Nullable(String)
street Nullable(String)
locality Nullable(String)
town Nullable(String)
district Nullable(String)
county Nullable(String)
```
Let's see what the most expensive neighborhoods are:
```bash
./clickhouse local -q "
SELECT
town,
district,
count() AS c,
round(avg(price)) AS price,
bar(price, 0, 5000000, 100)
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/house_parquet/house_0.parquet')
GROUP BY
town,
district
HAVING c >= 100
ORDER BY price DESC
LIMIT 10"
```
```response
LONDON CITY OF LONDON 886 2271305 █████████████████████████████████████████████▍
VIRGINIA WATER RUNNYMEDE 356 789301 ███████████████▊
BARNET ENFIELD 282 740514 ██████████████▊
NORTHWOOD THREE RIVERS 184 731609 ██████████████▋
```
:::tip
When you are ready to insert your files into ClickHouse, startup a ClickHouse server and insert the results of your `file` and `s3` table functions into a `MergeTree` table. View the [Quick Start](../../quick-start.mdx) for more details.
By default `clickhouse-local` has access to data of a ClickHouse server on the same host, and it does not depend on the server's configuration. It also supports loading server configuration using `--config-file` argument. For temporary data, a unique temporary data directory is created by default.
You don't have to use `stdin` or `--file` argument, and can open any number of files using the [`file` table function](../../sql-reference/table-functions/file.md):
- [Extracting, converting, and querying data in local files using clickhouse-local](https://clickhouse.com/blog/extracting-converting-querying-local-files-with-sql-clickhouse-local)
- [Getting Data Into ClickHouse - Part 1](https://clickhouse.com/blog/getting-data-into-clickhouse-part-1)
- [Exploring massive, real-world data sets: 100+ Years of Weather Records in ClickHouse](https://clickhouse.com/blog/real-world-data-noaa-climate-data)
