Ingest data from S3 buckets

The S3 connector does not guarantee the sequential reading of files or complete file reading.

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Syntax

CREATE SOURCE [ IF NOT EXISTS ] source_name
schema_definition
[INCLUDE { file | offset } [AS <column_name>]]
WITH (
   connector='s3',
   connector_parameter='value', ...
)
FORMAT data_format ENCODE data_encode (
   without_header = 'true' | 'false',
   delimiter = 'delimiter'
);

schema_definition:

(
   column_name data_type [ PRIMARY KEY ], ...
   [ PRIMARY KEY ( column_name, ... ) ]
)

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Parameters

note

Empty cells in CSV files will be parsed to NULL.

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Additional columns

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Examples

Here are examples of connecting RisingWave to an S3 source to read data from individual streams.

• CSV
• JSON
• PARQUET

CREATE TABLE s(
    id int,
    name varchar,
    age int
)
WITH (
    connector = 's3',
    s3.region_name = 'ap-southeast-2',
    s3.bucket_name = 'example-s3-source',
    s3.credentials.access = 'xxxxx',
    s3.credentials.secret = 'xxxxx'
) FORMAT PLAIN ENCODE CSV (
    without_header = 'true',
    delimiter = ',' -- set delimiter = E'\t' for tab-separated files
);

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Important considerations

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Object filtering in S3 buckets

RisingWave has a prefix argument designed for filtering objects in the S3 bucket. It relies on Apache Opendal whose prefix filter implementation is expected to be released soon.

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Handle new files in the bucket

RisingWave automatically ingests new files added to the bucket. However, it does not detect updates to a file if a file is deleted and a new file with the same name is added simultaneously. Additionally, RisingWave will ignore file deletions.

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Read data from the source

You need to create a materialized view from the source or create a table with the S3 connector to read the data. Here are some examples:

-- Create a materialized view from the source
CREATE SOURCE s3_source WITH ( connector = 's3', ... );
CREATE MATERIALIZED VIEW mv AS SELECT * FROM s3_source;

-- Create a table with the S3 connector
CREATE TABLE s3_table ( ... ) WITH ( connector = 's3', ... );

To view the progress of the source, you can also read the source directly

-- Create a materialized view from the source
CREATE SOURCE s3_source WITH ( connector = 's3_v2', ... );
SELECT count(*) from s3_source;

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Read Parquet files from S3

You can use the table function file_scan() to read Parquet files from S3, either a single file or a directory of Parquet files.

Function signature

file_scan(file_format, storage_type, s3_region, s3_access_key, s3_secret_key, file_location_or_directory)

For example, assume you have a Parquet file named sales_data.parquet that stores a company’s sales data, containing the following fields:

• product_id: Product ID
• sales_date: Sales date
• quantity: Sales quantity
• revenue: Sales revenue

You can use the following SQL statement to read this Parquet file:

Read a single Parquet file

SELECT
  product_id,
  sales_date,
  quantity,
  revenue
FROM file_scan(
  'parquet',
  's3',
  'ap-southeast-2',
  'xxxxxxxxxx',
  'yyyyyyyy',
  's3://your-bucket/path/to/sales_data.parquet'
);

----RESULT
product_id |  sales_date  | quantity | revenue
------------+-------------+----------+----------
         12 | 2023-04-01   |       50 |   1000.00
         12 | 2023-04-02   |       30 |    600.00
         15 | 2023-04-01   |       20 |    400.00
(3 rows)

If you have several such Parquet files, you can also read by their file directory:

Read a directory of Parquet files

SELECT
  product_id,
  sales_date,
  quantity,
  revenue
FROM file_scan(
  'parquet',
  's3',
  'ap-southeast-2',
  'xxxxxxxxxx',
  'yyyyyyyy',
  's3://your-bucket/path/to/sales_data_file_directory/'
);

----RESULT
product_id |  sales_date  | quantity | revenue
------------+-------------+----------+----------
         12 | 2023-04-01   |       50 |   1000.00
         12 | 2023-04-02   |       30 |    600.00
         15 | 2023-04-01   |       20 |    400.00
         15 | 2023-04-03   |       40 |    800.00
         18 | 2023-04-02   |       25 |    500.00
         18 | 2023-04-04   |       35 |    700.00
(6 rows)

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Handle unexpected file types or poorly formatted files

RisingWave will attempt to interpret and parse files, regardless of their type, as CSV or ndjson, based on the specified rules. Warnings will be reported for parts of the file that cannot be parsed, but the source part will not fail. Poorly formatted parts of a file will be discarded.