├── .gitignore ├── src ├── Procfile ├── requirements.txt └── main.py ├── local_test.sh ├── test_data └── customers │ ├── L1_Customer_details_raw_part-00000-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet │ ├── L1_Customer_details_raw_part-00001-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet │ ├── L1_Customer_details_raw_part-00002-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet │ └── L1_Customer_details_raw_part-00003-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet ├── README.md └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | src/local_test_env/ 2 | src/.scratchpad -------------------------------------------------------------------------------- /src/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 main:app -------------------------------------------------------------------------------- /src/requirements.txt: -------------------------------------------------------------------------------- 1 | sql-metadata 2 | argparse 3 | duckdb 4 | pyarrow 5 | Flask 6 | gunicorn 7 | google-cloud-storage 8 | pandas -------------------------------------------------------------------------------- /local_test.sh: -------------------------------------------------------------------------------- 1 | cd src 2 | pip3 install --upgrade pip 3 | python3 -m venv local_test_env 4 | source local_test_env/bin/activate 5 | pip3 install -r requirements.txt 6 | python3 main.py 7 | -------------------------------------------------------------------------------- /test_data/customers/L1_Customer_details_raw_part-00000-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/velascoluis/serverless-duckdb/HEAD/test_data/customers/L1_Customer_details_raw_part-00000-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet -------------------------------------------------------------------------------- /test_data/customers/L1_Customer_details_raw_part-00001-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/velascoluis/serverless-duckdb/HEAD/test_data/customers/L1_Customer_details_raw_part-00001-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet -------------------------------------------------------------------------------- /test_data/customers/L1_Customer_details_raw_part-00002-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/velascoluis/serverless-duckdb/HEAD/test_data/customers/L1_Customer_details_raw_part-00002-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet -------------------------------------------------------------------------------- /test_data/customers/L1_Customer_details_raw_part-00003-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/velascoluis/serverless-duckdb/HEAD/test_data/customers/L1_Customer_details_raw_part-00003-fc7d6e20-dbda-4143-91b5-d9414310dfd1-c000.snappy.parquet -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DuckDB Serverless deployment at GCP 2 | 3 | This repository contains code to deploy a [Cloud Run serverless endpoint](https://cloud.google.com/run) based on [duckDB](https://duckdb.org/) that is able to execute arbitrary SQL queries. The execution workflow is: 4 | - Data (tables) should be staged on [GCS](https://cloud.google.com/storage) in [PARQUET](https://parquet.apache.org/) format 5 | - If there is already a [duckdb catalog file](https://duckdb.org/docs/connect) it should be staged as well at GCS, it will be read upon start and uploaded at the very end to save any potential changes (e.g. table creation) 6 | - As a new query arrives, we parse the referenced tables and create a global python dict loading the corresponding Parquet tables into [Arrow Datasets](https://arrow.apache.org/docs/python/dataset.html), which are then registered at duckDB. If the container is still alive, subsequenta calls can reuse the [global python dict to avoid scanning the tables over and over](https://cloud.google.com/run/docs/tips/general#using_global_variables). 7 | - Data is sent back in JSON format 8 | 9 | ## Deployment 10 | 11 | - Clone repository: 12 | ```bash 13 | $> git clone https://github.com/velascoluis/serverless-duckdb.git 14 | $> cd serverless-duckdb 15 | ``` 16 | - Launch a cloud Shell with an user with enough permisions (e.g. project owner) and create a new GCS bucket and upload demo customers table: 17 | 18 | ```bash 19 | $> gsutil mb -c standard -l us-central1 gs:// 20 | $> gsutil cp -R test_data/customers/ gs:///data/customers 21 | ``` 22 | - Build the Cloud Run service, edit the `build_cloud_run.sh` adapting the variables: 23 | - `SERVICE_NAME` 24 | - `REGION` 25 | - `PROJECT_ID` 26 | ```bash 27 | $> ./build_cloud_run.sh 28 | ```` 29 | 30 | - Wait for the service to be deployed and copy the URL, then execute a query against the endpoint, you need to provide 3 parameters: 31 | - `sql_query`: The SQL to execute 32 | - `bucket_name`: The bucket with the data 33 | - `db_file`: Your duckDB catalog dbfile, a new one will be created the first time you use the database. 34 | 35 | ## Example use 36 | 37 | From a terminal run the following command changing CLOUD_RUN_ENDPOINT, BUCKET_NAME and DB_FILE_NAME 38 | 39 | ```bash 40 | #First call 41 | $> time curl -X GET 'https:///?sql_query=select%20count(gender),%20gender%20from%20customers%20where%20PhoneService=%27Yes%27%20group%20by%20gender&bucket_name=&db_file=' 42 | [{"count(gender)":2163,"gender":"Female"},{"count(gender)":2300,"gender":"Male"}] 43 | real 0m3.613s 44 | user 0m0.017s 45 | sys 0m0.014s 46 | #Extract from container log: 47 | #2022-08-30T09:15:00.296854ZINFO:root:Loading table:customers 48 | 49 | #Second call 50 | $> time curl -X GET 'https:///?sql_query=select%20count(gender),%20gender%20from%20customers%20where%20PhoneService=%27Yes%27%20group%20by%20gender&bucket_name=&db_file=' 51 | [{"count(gender)":2300,"gender":"Male"},{"count(gender)":2163,"gender":"Female"}] 52 | real 0m0.967s 53 | user 0m0.015s 54 | sys 0m0.011s 55 | #Extract from container log: 56 | #2022-08-30T09:15:03.342777ZINFO:root:Table:customers already in memory 57 | ``` 58 | ## References and kudos 59 | https://datamonkeysite.com/2022/08/27/running-a-serverless-duckdb-on-google-cloud/ 60 | -------------------------------------------------------------------------------- /src/main.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import pandas 3 | import json 4 | import os 5 | import logging 6 | 7 | from google.cloud import storage 8 | from flask import Flask, request 9 | from sql_metadata import Parser 10 | import pyarrow.dataset as ds 11 | import duckdb 12 | 13 | 14 | DATA_FORMAT = "parquet" 15 | #Global variable 16 | tables_dict = {} 17 | app = Flask(__name__) 18 | 19 | def exec_sql(sql_query,bucket_name,db_file): 20 | #Get db file name 21 | storage_client = storage.Client() 22 | bucket = storage_client.get_bucket(bucket_name) 23 | db_file_blob = bucket.blob("metadb/{}".format(db_file)) 24 | if db_file_blob.exists(): 25 | logging.info('duck db definition file found under metadb/{}, it will be reused'.format(db_file)) 26 | db_file_blob.download_to_filename(db_file) 27 | else: 28 | logging.info('duck db definition NOT found under metadb/{},a new one will be generated'.format(db_file)) 29 | #Extract tables from sql query and load into arrow datasets 30 | con = duckdb.connect(database=db_file, read_only=False) 31 | table_names = Parser(sql_query).tables 32 | for table_name in table_names: 33 | if any(storage_client.list_blobs(bucket_name, prefix="data/{}".format(table_name))): 34 | if not table_name in tables_dict: 35 | logging.info('Loading table:{}'.format(table_name)) 36 | tables_dict[table_name] = ds.dataset("gs://{}/data/{}".format(bucket_name,table_name),format=DATA_FORMAT) 37 | else: 38 | logging.info('Table:{} already in memory'.format(table_name)) 39 | else: 40 | return 'table not found : {}'.format(table_name), 400 41 | #Exec query and return results 42 | con.register(table_name, tables_dict[table_name]) 43 | con.execute(sql_query) 44 | df = con.fetchdf() 45 | logging.info('Uploading back metadb/{}'.format(db_file)) 46 | db_file_blob.upload_from_filename(db_file) 47 | return df.to_json(orient="records") 48 | 49 | 50 | @app.get("/") 51 | def main(): 52 | """ Executes a SQL query using duckdb as engine. 53 | :param str sql_query: The SQL query to exec 54 | :param str bucket_name: A GCS bucket containing both the data (under data directory) and the duckdb definition file (under metadb) 55 | It is expected that each of the tables beign referenced by the SQL query to exists under the data/ directory on PARQUET format 56 | :param str db_file: The name of the duckDB definition file 57 | :return: SQL query results 58 | :rtype: dict 59 | :raises BadRequest 60 | Example call: 61 | http://127.0.0.1:8080/?sql_query=select%20*%20from%20customers&bucket_name=velascoluis-dev-sandbox-warehouse&db_file=my_duckdb.db 62 | """ 63 | logging.getLogger().setLevel(logging.INFO) 64 | logging.info('Executing SQL with duckDB ..') 65 | 66 | if 'sql_query' in request.args: 67 | sql_query = request.args.get("sql_query",type=str) 68 | logging.info('sql_query:{}'.format(sql_query)) 69 | else: 70 | return 'sql_query not provided - bad request!', 400 71 | if 'bucket_name' in request.args: 72 | bucket_name = request.args.get("bucket_name",type=str) 73 | logging.info('bucket_name:{}'.format(bucket_name)) 74 | else: 75 | return 'bucket_name not provided - bad request!', 400 76 | if 'db_file' in request.args: 77 | db_file = request.args.get("db_file",type=str) 78 | logging.info('db_file:{}'.format(db_file)) 79 | else: 80 | return 'db_file not provided - bad request!', 400 81 | 82 | return exec_sql(sql_query,bucket_name,db_file) 83 | 84 | if __name__ == "__main__": 85 | app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) 86 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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