Command line SQL console

The DataFusion CLI is a command-line interactive SQL utility for executing queries against any supported data files. It is a convenient way to try DataFusion’s SQL support with your own data.

Installation

Install and run using Cargo

The easiest way to install DataFusion CLI a spin is via cargo install datafusion-cli.

Install and run using Homebrew (on MacOS)

DataFusion CLI can also be installed via Homebrew (on MacOS). If you don’t have Homebrew installed, you can check how to install it here.

Install it as any other pre-built software like this:

brew install datafusion
# ==> Downloading https://ghcr.io/v2/homebrew/core/datafusion/manifests/12.0.0
# ######################################################################## 100.0%
# ==> Downloading https://ghcr.io/v2/homebrew/core/datafusion/blobs/sha256:9ecc8a01be47ceb9a53b39976696afa87c0a8
# ==> Downloading from https://pkg-containers.githubusercontent.com/ghcr1/blobs/sha256:9ecc8a01be47ceb9a53b39976
# ######################################################################## 100.0%
# ==> Pouring datafusion--12.0.0.big_sur.bottle.tar.gz
# 🍺  /usr/local/Cellar/datafusion/12.0.0: 9 files, 17.4MB

datafusion-cli

Install and run using PyPI

DataFusion CLI can also be installed via PyPI. You can check how to install PyPI here.

Install it as any other pre-built software like this:

pip3 install datafusion
# Defaulting to user installation because normal site-packages is not writeable
# Collecting datafusion
#   Downloading datafusion-33.0.0-cp38-abi3-macosx_11_0_arm64.whl.metadata (9.6 kB)
# Collecting pyarrow>=11.0.0 (from datafusion)
#   Downloading pyarrow-14.0.1-cp39-cp39-macosx_11_0_arm64.whl.metadata (3.0 kB)
# Requirement already satisfied: numpy>=1.16.6 in /Users/Library/Python/3.9/lib/python/site-packages (from pyarrow>=11.0.0->datafusion) (1.23.4)
# Downloading datafusion-33.0.0-cp38-abi3-macosx_11_0_arm64.whl (13.5 MB)
#    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.5/13.5 MB 3.6 MB/s eta 0:00:00
# Downloading pyarrow-14.0.1-cp39-cp39-macosx_11_0_arm64.whl (24.0 MB)
#    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.0/24.0 MB 36.4 MB/s eta 0:00:00
# Installing collected packages: pyarrow, datafusion
#   Attempting uninstall: pyarrow
#     Found existing installation: pyarrow 10.0.1
#     Uninstalling pyarrow-10.0.1:
#       Successfully uninstalled pyarrow-10.0.1
# Successfully installed datafusion-33.0.0 pyarrow-14.0.1

datafusion-cli

Run using Docker

There is no officially published Docker image for the DataFusion CLI, so it is necessary to build from source instead.

Use the following commands to clone this repository and build a Docker image containing the CLI tool. Note that there is .dockerignore file in the root of the repository that may need to be deleted in order for this to work.

git clone https://github.com/apache/arrow-datafusion
cd arrow-datafusion
git checkout 12.0.0
docker build -f datafusion-cli/Dockerfile . --tag datafusion-cli
docker run -it -v $(your_data_location):/data datafusion-cli

Usage

See the current usage using datafusion-cli --help:

Apache Arrow <dev@arrow.apache.org>
Command Line Client for DataFusion query engine.

USAGE:
    datafusion-cli [OPTIONS]

OPTIONS:
    -b, --batch-size <BATCH_SIZE>
            The batch size of each query, or use DataFusion default

    -c, --command <COMMAND>...
            Execute the given command string(s), then exit

        --color
            Enables console syntax highlighting

    -f, --file <FILE>...
            Execute commands from file(s), then exit

        --format <FORMAT>
            [default: table] [possible values: csv, tsv, table, json, nd-json]

    -h, --help
            Print help information

    -m, --memory-limit <MEMORY_LIMIT>
            The memory pool limitation (e.g. '10g'), default to None (no limit)

        --maxrows <MAXROWS>
            The max number of rows to display for 'Table' format
            [default: 40] [possible values: numbers(0/10/...), inf(no limit)]

        --mem-pool-type <MEM_POOL_TYPE>
            Specify the memory pool type 'greedy' or 'fair', default to 'greedy'

    -p, --data-path <DATA_PATH>
            Path to your data, default to current directory

    -q, --quiet
            Reduce printing other than the results and work quietly

    -r, --rc <RC>...
            Run the provided files on startup instead of ~/.datafusionrc

    -V, --version
            Print version information

Querying data from the files directly

Files can be queried directly by enclosing the file, directory name or a remote location in single ' quotes as shown in the examples.

Examples

Create a CSV file to query.

$ echo "a,b" > data.csv
$ echo "1,2" >> data.csv

Query that single file (the CLI also supports parquet, compressed csv, avro, json and more)

$ datafusion-cli
DataFusion CLI v17.0.0
❯ select * from 'data.csv';
+---+---+
| a | b |
+---+---+
| 1 | 2 |
+---+---+
1 row in set. Query took 0.007 seconds.

You can also query directories of files with compatible schemas:

$ ls data_dir/
data.csv   data2.csv
$ datafusion-cli
DataFusion CLI v16.0.0
❯ select * from 'data_dir';
+---+---+
| a | b |
+---+---+
| 3 | 4 |
| 1 | 2 |
+---+---+
2 rows in set. Query took 0.007 seconds.

You can also query directly from any remote location supported by DataFusion without registering the location as a table. For example, to read from a remote parquet file via HTTP(S) you can use the following:

select count(*) from 'https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_1.parquet'
+----------+
| COUNT(*) |
+----------+
| 1000000  |
+----------+
1 row in set. Query took 0.595 seconds.

To read from an AWS S3 or GCS, use s3 or gs as a protocol prefix. For example, this will read a file
in S3 bucket named my-data-bucket. Note that this is not a real file location and therefore the query will fail, you need to use your own file location in S3. Also, you need to set the relevent access credentials as environmental variables (e.g. for AWS S3 you need to at least AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY)

select count(*) from 's3://my-data-bucket/athena_partitioned/hits.parquet'

Creating External Tables

It is also possible to create a table backed by files by explicitly via CREATE EXTERNAL TABLE as shown below. Filemask wildcards supported

Registering Parquet Data Sources

Parquet data sources can be registered by executing a CREATE EXTERNAL TABLE SQL statement. The schema information will be derived automatically.

Register a single file parquet datasource

CREATE EXTERNAL TABLE taxi
STORED AS PARQUET
LOCATION '/mnt/nyctaxi/tripdata.parquet';

Register a single folder parquet datasource. All files inside must be valid parquet files!

CREATE EXTERNAL TABLE taxi
STORED AS PARQUET
LOCATION '/mnt/nyctaxi/';

Register a single folder parquet datasource by specifying a wildcard for files to read

CREATE EXTERNAL TABLE taxi
STORED AS PARQUET
LOCATION '/mnt/nyctaxi/*.parquet';

Registering CSV Data Sources

CSV data sources can be registered by executing a CREATE EXTERNAL TABLE SQL statement.

CREATE EXTERNAL TABLE test
STORED AS CSV
WITH HEADER ROW
LOCATION '/path/to/aggregate_test_100.csv';

It is also possible to provide schema information.

CREATE EXTERNAL TABLE test (
    c1  VARCHAR NOT NULL,
    c2  INT NOT NULL,
    c3  SMALLINT NOT NULL,
    c4  SMALLINT NOT NULL,
    c5  INT NOT NULL,
    c6  BIGINT NOT NULL,
    c7  SMALLINT NOT NULL,
    c8  INT NOT NULL,
    c9  BIGINT NOT NULL,
    c10 VARCHAR NOT NULL,
    c11 FLOAT NOT NULL,
    c12 DOUBLE NOT NULL,
    c13 VARCHAR NOT NULL
)
STORED AS CSV
LOCATION '/path/to/aggregate_test_100.csv';

Registering Remote Data Sources

datafusion-cli can read from remote locations using a variety of protocols. For example to read from a remote parquet file via HTTP(S) you can use the following:

CREATE EXTERNAL TABLE hits
STORED AS PARQUET
LOCATION 'https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_1.parquet';
select count(*) from hits;
+----------+
| COUNT(*) |
+----------+
| 1000000  |
+----------+
1 row in set. Query took 0.344 seconds.

Registering S3 Data Sources

AWS S3 data sources can be registered by executing a CREATE EXTERNAL TABLE SQL statement.

CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
    'access_key_id' '******',
    'secret_access_key' '******',
    'region' 'us-east-2'
)
LOCATION 's3://bucket/path/file.parquet';

The supported OPTIONS are:

  • access_key_id

  • secret_access_key

  • session_token

  • region

It is also possible to simplify sql statements by environment variables.

$ export AWS_DEFAULT_REGION=us-east-2
$ export AWS_SECRET_ACCESS_KEY=******
$ export AWS_ACCESS_KEY_ID=******

$ datafusion-cli
DataFusion CLI v21.0.0
❯ create external table test stored as parquet location 's3://bucket/path/file.parquet';
0 rows in set. Query took 0.374 seconds.
❯ select * from test;
+----------+----------+
| column_1 | column_2 |
+----------+----------+
| 1        | 2        |
+----------+----------+
1 row in set. Query took 0.171 seconds.

Details of the environment variables that can be used are:

Registering OSS Data Sources

Alibaba cloud OSS data sources can be registered by executing a CREATE EXTERNAL TABLE SQL statement.

CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
    'access_key_id' '******',
    'secret_access_key' '******',
    'endpoint' 'https://bucket.oss-cn-hangzhou.aliyuncs.com'
)
LOCATION 'oss://bucket/path/file.parquet';

The supported OPTIONS are:

  • access_key_id

  • secret_access_key

  • endpoint

Note that the endpoint format of oss needs to be: https://{bucket}.{oss-region-endpoint}

Registering GCS Data Sources

Google Cloud Storage data sources can be registered by executing a CREATE EXTERNAL TABLE SQL statement.

CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
    'service_account_path' '/tmp/gcs.json',
)
LOCATION 'gs://bucket/path/file.parquet';

The supported OPTIONS are:

  • service_account_path -> location of service account file

  • service_account_key -> JSON serialized service account key

  • application_credentials_path -> location of application credentials file

It is also possible to simplify sql statements by environment variables.

$ export GOOGLE_SERVICE_ACCOUNT=/tmp/gcs.json

$ datafusion-cli
DataFusion CLI v21.0.0
❯ create external table test stored as parquet location 'gs://bucket/path/file.parquet';
0 rows in set. Query took 0.374 seconds.
❯ select * from test;
+----------+----------+
| column_1 | column_2 |
+----------+----------+
| 1        | 2        |
+----------+----------+
1 row in set. Query took 0.171 seconds.

Details of the environment variables that can be used are:

  • GOOGLE_SERVICE_ACCOUNT: location of service account file

  • GOOGLE_SERVICE_ACCOUNT_PATH: (alias) location of service account file

  • SERVICE_ACCOUNT: (alias) location of service account file

  • GOOGLE_SERVICE_ACCOUNT_KEY: JSON serialized service account key

  • GOOGLE_BUCKET: bucket name

  • GOOGLE_BUCKET_NAME: (alias) bucket name

Commands

Available commands inside DataFusion CLI are:

  • Quit

> \q
  • Help

> \?
  • ListTables

> \d
  • DescribeTable

> \d table_name
  • QuietMode

> \quiet [true|false]
  • list function

> \h
  • Search and describe function

> \h function

Supported SQL

In addition to the normal SQL supported in DataFusion, datafusion-cli also supports additional statements and commands:

  • Show configuration options

SHOW ALL [VERBOSE]

> show all;

+-------------------------------------------------+---------+
| name                                            | value   |
+-------------------------------------------------+---------+
| datafusion.execution.batch_size                 | 8192    |
| datafusion.execution.coalesce_batches           | true    |
| datafusion.execution.time_zone                  | UTC     |
| datafusion.explain.logical_plan_only            | false   |
| datafusion.explain.physical_plan_only           | false   |
| datafusion.optimizer.filter_null_join_keys      | false   |
| datafusion.optimizer.skip_failed_rules          | true    |
+-------------------------------------------------+---------+
  • Show specific configuration option

SHOW xyz.abc.qwe [VERBOSE]

> show datafusion.execution.batch_size;

+-------------------------------------------------+---------+
| name                                            | value   |
+-------------------------------------------------+---------+
| datafusion.execution.batch_size                 | 8192    |
+-------------------------------------------------+---------+
  • Set configuration options

> SET datafusion.execution.batch_size to 1024;
  • parquet_metadata table function

The parquet_metadata table function can be used to inspect detailed metadata about a parquet file such as statistics, sizes, and other information. This can be helpful to understand how parquet files are structured.

For example, to see information about the "WatchID" column in the hits.parquet file, you can use:

SELECT path_in_schema, row_group_id, row_group_num_rows, stats_min, stats_max, total_compressed_size
FROM parquet_metadata('hits.parquet')
WHERE path_in_schema = '"WatchID"'
LIMIT 3;

+----------------+--------------+--------------------+---------------------+---------------------+-----------------------+
| path_in_schema | row_group_id | row_group_num_rows | stats_min           | stats_max           | total_compressed_size |
+----------------+--------------+--------------------+---------------------+---------------------+-----------------------+
| "WatchID"      | 0            | 450560             | 4611687214012840539 | 9223369186199968220 | 3883759               |
| "WatchID"      | 1            | 612174             | 4611689135232456464 | 9223371478009085789 | 5176803               |
| "WatchID"      | 2            | 344064             | 4611692774829951781 | 9223363791697310021 | 3031680               |
+----------------+--------------+--------------------+---------------------+---------------------+-----------------------+
3 rows in set. Query took 0.053 seconds.

The returned table has the following columns for each row for each column chunk in the file. Please refer to the Parquet Documentation for more information.

column_name

data_type

Description

filename

Utf8

Name of the file

row_group_id

Int64

Row group index the column chunk belongs to

row_group_num_rows

Int64

Count of rows stored in the row group

row_group_num_columns

Int64

Total number of columns in the row group (same for all row groups)

row_group_bytes

Int64

Number of bytes used to store the row group (not including metadata)

column_id

Int64

ID of the column

file_offset

Int64

Offset within the file that this column chunk’s data begins

num_values

Int64

Total number of values in this column chunk

path_in_schema

Utf8

“Path” (column name) of the column chunk in the schema

type

Utf8

Parquet data type of the column chunk

stats_min

Utf8

The minimum value for this column chunk, if stored in the statistics, cast to a string

stats_max

Utf8

The maximum value for this column chunk, if stored in the statistics, cast to a string

stats_null_count

Int64

Number of null values in this column chunk, if stored in the statistics

stats_distinct_count

Int64

Number of distinct values in this column chunk, if stored in the statistics

stats_min_value

Utf8

Same as stats_min

stats_max_value

Utf8

Same as stats_max

compression

Utf8

Block level compression (e.g. SNAPPY) used for this column chunk

encodings

Utf8

All block level encodings (e.g. [PLAIN_DICTIONARY, PLAIN, RLE]) used for this column chunk

index_page_offset

Int64

Offset in the file of the page index, if any

dictionary_page_offset

Int64

Offset in the file of the dictionary page, if any

data_page_offset

Int64

Offset in the file of the first data page, if any

total_compressed_size

Int64

Number of bytes the column chunk’s data after encoding and compression (what is stored in the file)

total_uncompressed_size

Int64

Number of bytes the column chunk’s data after encoding

+————————-+———–+————-+

Changing Configuration Options

All available configuration options can be seen using SHOW ALL as described above.

You can change the configuration options using environment variables. datafusion-cli looks in the corresponding environment variable with an upper case name and all . converted to _.

For example, to set datafusion.execution.batch_size to 1024 you would set the DATAFUSION_EXECUTION_BATCH_SIZE environment variable appropriately:

$ DATAFUSION_EXECUTION_BATCH_SIZE=1024 datafusion-cli
DataFusion CLI v12.0.0
❯ show all;
+-------------------------------------------------+---------+
| name                                            | value   |
+-------------------------------------------------+---------+
| datafusion.execution.batch_size                 | 1024    |
| datafusion.execution.coalesce_batches           | true    |
| datafusion.execution.time_zone                  | UTC     |
| datafusion.explain.logical_plan_only            | false   |
| datafusion.explain.physical_plan_only           | false   |
| datafusion.optimizer.filter_null_join_keys      | false   |
| datafusion.optimizer.skip_failed_rules          | true    |
+-------------------------------------------------+---------+
8 rows in set. Query took 0.002 seconds.

You can change the configuration options using SET statement as well

$ datafusion-cli
DataFusion CLI v13.0.0
❯ show datafusion.execution.batch_size;
+---------------------------------+---------+
| name                            | value   |
+---------------------------------+---------+
| datafusion.execution.batch_size | 8192    |
+---------------------------------+---------+
1 row in set. Query took 0.011 seconds.

❯ set datafusion.execution.batch_size to 1024;
0 rows in set. Query took 0.000 seconds.

❯ show datafusion.execution.batch_size;
+---------------------------------+---------+
| name                            | value   |
+---------------------------------+---------+
| datafusion.execution.batch_size | 1024    |
+---------------------------------+---------+
1 row in set. Query took 0.005 seconds.