ClickHouse/docs/en/engines/table-engines/integrations/hdfs.md

---
toc_priority: 6
toc_title: HDFS
---

# HDFS {#table_engines-hdfs}

This engine provides integration with [Apache Hadoop](https://en.wikipedia.org/wiki/Apache_Hadoop) ecosystem by allowing to manage data on [HDFS](https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html) via ClickHouse. This engine is similar
to the [File](../../../engines/table-engines/special/file.md#table_engines-file) and [URL](../../../engines/table-engines/special/url.md#table_engines-url) engines, but provides Hadoop-specific features.

## Usage {#usage}

``` sql
ENGINE = HDFS(URI, format)
```

The `URI` parameter is the whole file URI in HDFS.
The `format` parameter specifies one of the available file formats. To perform
`SELECT` queries, the format must be supported for input, and to perform
`INSERT` queries – for output. The available formats are listed in the
[Formats](../../../interfaces/formats.md#formats) section.
The path part of `URI` may contain globs. In this case the table would be readonly.

**Example:**

**1.** Set up the `hdfs_engine_table` table:

``` sql
CREATE TABLE hdfs_engine_table (name String, value UInt32) ENGINE=HDFS('hdfs://hdfs1:9000/other_storage', 'TSV')
```

**2.** Fill file:

``` sql
INSERT INTO hdfs_engine_table VALUES ('one', 1), ('two', 2), ('three', 3)
```

**3.** Query the data:

``` sql
SELECT * FROM hdfs_engine_table LIMIT 2
```

``` text
┌─name─┬─value─┐
│ one  │     1 │
│ two  │     2 │
└──────┴───────┘
```

## Implementation Details {#implementation-details}

-   Reads and writes can be parallel.
-   Zero-copy replication is supported, which means that if the data is stored remotely on several machines and needs to be synchronized, then only the metadata is replicated (paths to the data parts), but not the data itself.  
-   Not supported:
    -   `ALTER` and `SELECT...SAMPLE` operations.
    -   Indexes.

**Globs in path**

Multiple path components can have globs. For being processed file should exists and matches to the whole path pattern. Listing of files determines during `SELECT` (not at `CREATE` moment).

-   `*` — Substitutes any number of any characters except `/` including empty string.
-   `?` — Substitutes any single character.
-   `{some_string,another_string,yet_another_one}` — Substitutes any of strings `'some_string', 'another_string', 'yet_another_one'`.
-   `{N..M}` — Substitutes any number in range from N to M including both borders.

Constructions with `{}` are similar to the [remote](../../../sql-reference/table-functions/remote.md) table function.

**Example**

1.  Suppose we have several files in TSV format with the following URIs on HDFS:

-   ‘hdfs://hdfs1:9000/some_dir/some_file_1’
-   ‘hdfs://hdfs1:9000/some_dir/some_file_2’
-   ‘hdfs://hdfs1:9000/some_dir/some_file_3’
-   ‘hdfs://hdfs1:9000/another_dir/some_file_1’
-   ‘hdfs://hdfs1:9000/another_dir/some_file_2’
-   ‘hdfs://hdfs1:9000/another_dir/some_file_3’

1.  There are several ways to make a table consisting of all six files:

<!-- -->

``` sql
CREATE TABLE table_with_range (name String, value UInt32) ENGINE = HDFS('hdfs://hdfs1:9000/{some,another}_dir/some_file_{1..3}', 'TSV')
```

Another way:

``` sql
CREATE TABLE table_with_question_mark (name String, value UInt32) ENGINE = HDFS('hdfs://hdfs1:9000/{some,another}_dir/some_file_?', 'TSV')
```

Table consists of all the files in both directories (all files should satisfy format and schema described in query):

``` sql
CREATE TABLE table_with_asterisk (name String, value UInt32) ENGINE = HDFS('hdfs://hdfs1:9000/{some,another}_dir/*', 'TSV')
```

!!! warning "Warning"
    If the listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use `?`.

**Example**

Create table with files named `file000`, `file001`, … , `file999`:

``` sql
CREATE TABLE big_table (name String, value UInt32) ENGINE = HDFS('hdfs://hdfs1:9000/big_dir/file{0..9}{0..9}{0..9}', 'CSV')
```
## Configuration {#configuration}

Similar to GraphiteMergeTree, the HDFS engine supports extended configuration using the ClickHouse config file. There are two configuration keys that you can use: global (`hdfs`) and user-level (`hdfs_*`). The global configuration is applied first, and then the user-level configuration is applied (if it exists).

``` xml
  <!-- Global configuration options for HDFS engine type -->
  <hdfs>
	<hadoop_kerberos_keytab>/tmp/keytab/clickhouse.keytab</hadoop_kerberos_keytab>
	<hadoop_kerberos_principal>clickuser@TEST.CLICKHOUSE.TECH</hadoop_kerberos_principal>
	<hadoop_security_authentication>kerberos</hadoop_security_authentication>
  </hdfs>

  <!-- Configuration specific for user "root" -->
  <hdfs_root>
	<hadoop_kerberos_principal>root@TEST.CLICKHOUSE.TECH</hadoop_kerberos_principal>
  </hdfs_root>
```

### Configuration Options {#configuration-options}

#### Supported by libhdfs3 {#supported-by-libhdfs3}


| **parameter**                                         | **default value**       |
| rpc\_client\_connect\_tcpnodelay                      | true                    |
| dfs\_client\_read\_shortcircuit                       | true                    |
| output\_replace-datanode-on-failure                   | true                    |
| input\_notretry-another-node                          | false                   |
| input\_localread\_mappedfile                          | true                    |
| dfs\_client\_use\_legacy\_blockreader\_local          | false                   |
| rpc\_client\_ping\_interval                           | 10  * 1000              |
| rpc\_client\_connect\_timeout                         | 600 * 1000              |
| rpc\_client\_read\_timeout                            | 3600 * 1000             |
| rpc\_client\_write\_timeout                           | 3600 * 1000             |
| rpc\_client\_socekt\_linger\_timeout                  | -1                      |
| rpc\_client\_connect\_retry                           | 10                      |
| rpc\_client\_timeout                                  | 3600 * 1000             |
| dfs\_default\_replica                                 | 3                       |
| input\_connect\_timeout                               | 600 * 1000              |
| input\_read\_timeout                                  | 3600 * 1000             |
| input\_write\_timeout                                 | 3600 * 1000             |
| input\_localread\_default\_buffersize                 | 1 * 1024 * 1024         |
| dfs\_prefetchsize                                     | 10                      |
| input\_read\_getblockinfo\_retry                      | 3                       |
| input\_localread\_blockinfo\_cachesize                | 1000                    |
| input\_read\_max\_retry                               | 60                      |
| output\_default\_chunksize                            | 512                     |
| output\_default\_packetsize                           | 64 * 1024               |
| output\_default\_write\_retry                         | 10                      |
| output\_connect\_timeout                              | 600 * 1000              |
| output\_read\_timeout                                 | 3600 * 1000             |
| output\_write\_timeout                                | 3600 * 1000             |
| output\_close\_timeout                                | 3600 * 1000             |
| output\_packetpool\_size                              | 1024                    |
| output\_heeartbeat\_interval                          | 10 * 1000               |
| dfs\_client\_failover\_max\_attempts                  | 15                      |
| dfs\_client\_read\_shortcircuit\_streams\_cache\_size | 256                     |
| dfs\_client\_socketcache\_expiryMsec                  | 3000                    |
| dfs\_client\_socketcache\_capacity                    | 16                      |
| dfs\_default\_blocksize                               | 64 * 1024 * 1024        |
| dfs\_default\_uri                                     | "hdfs://localhost:9000" |
| hadoop\_security\_authentication                      | "simple"                |
| hadoop\_security\_kerberos\_ticket\_cache\_path       | ""                      |
| dfs\_client\_log\_severity                            | "INFO"                  |
| dfs\_domain\_socket\_path                             | ""                      |


[HDFS Configuration Reference](https://hawq.apache.org/docs/userguide/2.3.0.0-incubating/reference/HDFSConfigurationParameterReference.html) might explain some parameters.


#### ClickHouse extras {#clickhouse-extras}

| **parameter**                                         | **default value**       |
|hadoop\_kerberos\_keytab                               | ""                      |
|hadoop\_kerberos\_principal                            | ""                      |
|hadoop\_kerberos\_kinit\_command                       | kinit                   |

#### Limitations {#limitations}
  * hadoop\_security\_kerberos\_ticket\_cache\_path can be global only, not user specific

## Kerberos support {#kerberos-support}

If hadoop\_security\_authentication parameter has value 'kerberos', ClickHouse authentifies via Kerberos facility.
Parameters [here](#clickhouse-extras) and hadoop\_security\_kerberos\_ticket\_cache\_path may be of help.
Note that due to libhdfs3 limitations only old-fashioned approach is supported,
datanode communications are not secured by SASL (HADOOP\_SECURE\_DN\_USER is a reliable indicator of such
security approach). Use tests/integration/test\_storage\_kerberized\_hdfs/hdfs_configs/bootstrap.sh for reference.

If hadoop\_kerberos\_keytab, hadoop\_kerberos\_principal or hadoop\_kerberos\_kinit\_command is specified, kinit will be invoked. hadoop\_kerberos\_keytab and hadoop\_kerberos\_principal are mandatory in this case. kinit tool and krb5 configuration files are required.

## Virtual Columns {#virtual-columns}

-   `_path` — Path to the file.
-   `_file` — Name of the file.

**See Also**

-   [Virtual columns](../../../engines/table-engines/index.md#table_engines-virtual_columns)

[Original article](https://clickhouse.tech/docs/en/engines/table-engines/integrations/hdfs/) <!--hide-->