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apache/datafusion/refs/heads/codex/stack-demo-setup/./docs/source/user-guide/cli/datasources.md
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# Local Files / Directories
Files can be queried directly by enclosing the file, directory name
or a remote location in single `'` quotes as shown in the examples.
Create a CSV file to query.
```shell
$ 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)
```shell
$ 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:
```shell
$ ls data_dir/
data.csv data2.csv
```
```shell
$ 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.
```
# Remote Files / Directories
You can also query directly 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:
```sql
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, to read a file in an S3 bucket named `my-data-bucket` use the URL
`s3://my-data-bucket`and set the relevant access credentials as environmental
variables (e.g. for AWS S3 you can use `AWS_ACCESS_KEY_ID` and
`AWS_SECRET_ACCESS_KEY`).
```sql
> select count(*) from 's3://altinity-clickhouse-data/nyc_taxi_rides/data/tripdata_parquet/';
+------------+
| count(*) |
+------------+
| 1310903963 |
+------------+
```
See the [`CREATE EXTERNAL TABLE`](#create-external-table) section below for
additional configuration options.
# `CREATE EXTERNAL TABLE`
It is also possible to create a table backed by files or remote locations via
`CREATE EXTERNAL TABLE` as shown below. Note that DataFusion does not support
wildcards (e.g. `*`) in file paths; instead, specify the directory path directly
to read all compatible files in that directory.
For example, to create a table `hits` backed by a local parquet file named `hits.parquet`:
```sql
CREATE EXTERNAL TABLE hits
STORED AS PARQUET
LOCATION 'hits.parquet';
```
To create a table `hits` backed by a remote parquet file via HTTP(S):
```sql
CREATE EXTERNAL TABLE hits
STORED AS PARQUET
LOCATION 'https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_1.parquet';
```
In both cases, `hits` now can be queried as a regular table:
```sql
select count(*) from hits;
+----------+
| COUNT(*) |
+----------+
| 1000000 |
+----------+
1 row in set. Query took 0.344 seconds.
```
## Reading from standard input
On Unix-like systems you can pipe data into the CLI and query it by pointing the
`LOCATION` at the `/dev/stdin` pseudo-file:
```console
$ cat hits.csv | datafusion-cli -c "
CREATE EXTERNAL TABLE hits STORED AS CSV LOCATION '/dev/stdin' OPTIONS ('format.has_header' 'true');
SELECT count(*) FROM hits;"
```
This works for CSV, JSON, and Parquet. Because standard input is not seekable
(and Parquet stores its metadata at the end of the file), the CLI buffers the
entire input into memory before querying it, so the data must fit in memory.
Standard input is read only once: the buffered contents are reused for any
further tables backed by `/dev/stdin` in the same session. Those tables must
declare the same `STORED AS` format as the first one; a differing format is
rejected with an error.
The SQL must be passed with `-c`/`--command` or `-f`/`--file` so that standard
input is free to carry the data. In the interactive shell (and when SQL is
piped to the CLI without `-c`/`-f`) standard input carries the SQL itself, and
`LOCATION '/dev/stdin'` returns an error.
**Why Wildcards Are Not Supported**
Although wildcards (e.g., _.parquet or \*\*/_.parquet) may work for local
filesystems in some cases, they are not supported by DataFusion CLI. This
is because wildcards are not universally applicable across all storage backends
(e.g., S3, GCS). Instead, DataFusion expects the user to specify the directory
path, and it will automatically read all compatible files within that directory.
For example, the following usage is not supported:
```sql
CREATE EXTERNAL TABLE test (
message TEXT,
day DATE
)
STORED AS PARQUET
LOCATION 'gs://bucket/*.parquet';
```
Instead, you should use:
```sql
CREATE EXTERNAL TABLE test (
message TEXT,
day DATE
)
STORED AS PARQUET
LOCATION 'gs://bucket/my_table/';
```
When specifying a directory path that has a Hive compliant partition structure, by default, DataFusion CLI will
automatically parse and incorporate the Hive columns and their values into the table's schema and data. Given the
following remote object paths:
```console
gs://bucket/my_table/a=1/b=100/file1.parquet
gs://bucket/my_table/a=2/b=200/file2.parquet
```
`my_table` can be queried and filtered on the Hive columns:
```sql
CREATE EXTERNAL TABLE my_table
STORED AS PARQUET
LOCATION 'gs://bucket/my_table/';
SELECT count(*) FROM my_table WHERE b=200;
+----------+
| count(*) |
+----------+
| 1 |
+----------+
```
# Formats
## Parquet
The schema information for parquet will be derived automatically.
Register a single file parquet datasource
```sql
CREATE EXTERNAL TABLE taxi
STORED AS PARQUET
LOCATION '/mnt/nyctaxi/tripdata.parquet';
```
Register a single folder parquet datasource. Note: All files inside must be valid
parquet files and have compatible schemas
:::{note}
Paths must end in Slash `/`
: The path must end in `/` otherwise DataFusion will treat the path as a file and not a directory
:::
```sql
CREATE EXTERNAL TABLE taxi
STORED AS PARQUET
LOCATION '/mnt/nyctaxi/';
```
### Parquet Specific Options
You can specify additional options for parquet files using the `OPTIONS` clause.
For example, to read and write a parquet directory with encryption settings you could use:
```sql
CREATE EXTERNAL TABLE encrypted_parquet_table
(
double_field double,
float_field float
)
STORED AS PARQUET LOCATION 'pq/' OPTIONS (
-- encryption
'format.crypto.file_encryption.encrypt_footer' 'true',
'format.crypto.file_encryption.footer_key_as_hex' '30313233343536373839303132333435', -- b"0123456789012345"
'format.crypto.file_encryption.column_key_as_hex::double_field' '31323334353637383930313233343530', -- b"1234567890123450"
'format.crypto.file_encryption.column_key_as_hex::float_field' '31323334353637383930313233343531', -- b"1234567890123451"
-- decryption
'format.crypto.file_decryption.footer_key_as_hex' '30313233343536373839303132333435', -- b"0123456789012345"
'format.crypto.file_decryption.column_key_as_hex::double_field' '31323334353637383930313233343530', -- b"1234567890123450"
'format.crypto.file_decryption.column_key_as_hex::float_field' '31323334353637383930313233343531', -- b"1234567890123451"
);
```
Here the keys are specified in hexadecimal format because they are binary data. These can be encoded in SQL using:
```sql
select encode('0123456789012345', 'hex');
/*
+----------------------------------------------+
| encode(Utf8("0123456789012345"),Utf8("hex")) |
+----------------------------------------------+
| 30313233343536373839303132333435 |
+----------------------------------------------+
*/
```
For more details on the available options, refer to the Rust
[TableParquetOptions](https://docs.rs/datafusion/latest/datafusion/common/config/struct.TableParquetOptions.html)
documentation in DataFusion.
## CSV
DataFusion will infer the CSV schema automatically or you can provide it explicitly.
Register a single file csv datasource with a header row:
```sql
CREATE EXTERNAL TABLE test
STORED AS CSV
LOCATION '/path/to/aggregate_test_100.csv'
OPTIONS ('has_header' 'true');
```
Register a single file csv datasource with explicitly defined schema:
```sql
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';
```
# Locations
## HTTP(s)
To read from a remote parquet file via HTTP(S):
```sql
CREATE EXTERNAL TABLE hits
STORED AS PARQUET
LOCATION 'https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_1.parquet';
```
## S3
DataFusion CLI supports configuring [AWS S3](https://aws.amazon.com/s3/) via the
`CREATE EXTERNAL TABLE` statement and standard AWS configuration methods (via the
[`aws-config`] AWS SDK crate).
To create an external table from a file in an S3 bucket with explicit
credentials:
```sql
CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
'aws.access_key_id' '******',
'aws.secret_access_key' '******',
'aws.region' 'us-east-2'
)
LOCATION 's3://bucket/path/file.parquet';
```
To create an external table using environment variables:
```bash
$ 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 CREATE 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.
```
To read from a public S3 bucket without signatures, use the
`aws.SKIP_SIGNATURE` option:
```sql
CREATE EXTERNAL TABLE nyc_taxi_rides
STORED AS PARQUET LOCATION 's3://altinity-clickhouse-data/nyc_taxi_rides/data/tripdata_parquet/'
OPTIONS(aws.SKIP_SIGNATURE true);
```
Credentials are taken in this order of precedence:
1. Explicitly specified in the `OPTIONS` clause of the `CREATE EXTERNAL TABLE` statement.
2. Determined by [`aws-config`] crate (standard environment variables such as `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` as well as other AWS specific features).
If no credentials are specified, DataFusion CLI will use unsigned requests to S3,
which allows reading from public buckets.
Supported configuration options are:
| Environment Variable | Configuration Option | Description |
| ---------------------------------------- | ----------------------- | ---------------------------------------------- |
| `AWS_ACCESS_KEY_ID` | `aws.access_key_id` | |
| `AWS_SECRET_ACCESS_KEY` | `aws.secret_access_key` | |
| `AWS_DEFAULT_REGION` | `aws.region` | |
| `AWS_ENDPOINT` | `aws.endpoint` | |
| `AWS_SESSION_TOKEN` | `aws.token` | |
| `AWS_CONTAINER_CREDENTIALS_RELATIVE_URI` | | See [IAM Roles] |
| `AWS_ALLOW_HTTP` | | If "true", permit HTTP connections without TLS |
| `AWS_SKIP_SIGNATURE` | `aws.skip_signature` | If "true", does not sign requests |
| | `aws.nosign` | Alias for `skip_signature` |
[iam roles]: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-iam-roles.html
[`aws-config`]: https://docs.rs/aws-config/latest/aws_config/
## OSS
[Alibaba cloud OSS](https://www.alibabacloud.com/product/object-storage-service) data sources must have connection credentials configured
```sql
CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
'aws.access_key_id' '******',
'aws.secret_access_key' '******',
'aws.oss.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}`
## COS
[Tencent cloud COS](https://cloud.tencent.com/product/cos) data sources data sources must have connection credentials configured
```sql
CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
'aws.access_key_id' '******',
'aws.secret_access_key' '******',
'aws.cos.endpoint' 'https://cos.ap-singapore.myqcloud.com'
)
LOCATION 'cos://bucket/path/file.parquet';
```
The supported OPTIONS are:
- access_key_id
- secret_access_key
- endpoint
Note that the `endpoint` format of urls must be: `https://cos.{cos-region-endpoint}`
## GCS
[Google Cloud Storage](https://cloud.google.com/storage) data sources must have connection credentials configured
For example, to create an external table from a file in a GCS bucket
```sql
CREATE EXTERNAL TABLE test
STORED AS PARQUET
OPTIONS(
'gcp.service_account_path' '/tmp/gcs.json',
)
LOCATION 'gs://bucket/path/file.parquet';
```
It is also possible to specify the access information using environment variables:
```bash
$ 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.
```
Supported configuration options are:
| Environment Variable | Configuration Option | Description |
| -------------------------------- | ---------------------------------- | ---------------------------------------- |
| `GOOGLE_SERVICE_ACCOUNT` | `gcp.service_account_path` | location of service account file |
| `GOOGLE_SERVICE_ACCOUNT_PATH` | `gcp.service_account_path` | (alias) location of service account file |
| `SERVICE_ACCOUNT` | `gcp.service_account_path` | (alias) location of service account file |
| `GOOGLE_SERVICE_ACCOUNT_KEY` | `gcp.service_account_key` | JSON serialized service account key |
| `GOOGLE_APPLICATION_CREDENTIALS` | `gcp.application_credentials_path` | location of application credentials file |
| `GOOGLE_BUCKET` | | bucket name |
| `GOOGLE_BUCKET_NAME` | | (alias) bucket name |