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https://github.com/dacort/athena-sqlite

A SQLite driver for S3 and Amazon Athena 😳
https://github.com/dacort/athena-sqlite

amazon-athena athena aws lambda-layer s3 sar serverless sqlite vfs

Last synced: 3 months ago
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A SQLite driver for S3 and Amazon Athena 😳

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README 

        
# Athena SQLite Driver



Using Athena's new [Query Federation](https://github.com/awslabs/aws-athena-query-federation/) functionality, read SQLite databases from S3.



Install it from the Serverless Application Repository: [AthenaSQLiteConnector](https://serverlessrepo.aws.amazon.com/#/applications/arn:aws:serverlessrepo:us-east-1:689449560910:applications~AthenaSQLITEConnector).



## Why?



I occasionally like to put together fun side projects over Thanksgiving and Christmas holidays.



I'd always joked it would a crazy idea to be able to read SQLite using Athena, so...here we are!



## How?



- I decided to use Python as I'm most familiar with it and because of the next point

- Using [APSW](https://rogerbinns.github.io/apsw/), we can implement a [Virtual File System](https://rogerbinns.github.io/apsw/vfs.html) (VFS) for S3

- Using the [Athena query federation example](https://github.com/awslabs/aws-athena-query-federation/blob/master/athena-example/), we can see what calls need to be implemented



The PyArrow library unfortunately weighs in over 250MB, so we have to use a custom compilation step to build a Lambda Layer.



## What?



Drop SQLite databases in a single prefix in S3, and Athena will list each file as a database and automatically detect tables and schemas.



Currently, all data types are strings. I'll fix this eventually.  All good things in time.



## Status



This project is under active development and very much in it's infancy.



Many things are hard-coded or broken into various pieces as I experiment and figure out how everything works.



## Building



The documentation for this is a work in progress. It's currently in between me creating the resources manually and building the assets for the AWS SAR,

and most of the docs will be automated away.



### Requirements



- Docker

- Python 3.7



### Lambda layer



First you need to build Lambda layer. There are two Dockerfiles and build scripts in the `lambda-layer/` directory.



We'll execute each of the build scripts and copy the results to the target directory. This is referenced by the SAR template, [`athena-sqlite.yaml`](athena-sqlite.yaml).



```

cd lambda-layer

./build.sh

./build-pyarrow.sh

cp -R layer/ ../target/

```



### Upload sample data



For the purpose of this test, we just have a sample sqlite database you can upload.



`aws s3 cp sample-data/sample_data.sqlite s3:////`



Feel free to upload your own SQLite databases as well!



### Lambda function



There are three components to the Lambda code:



- `vfs.py` - A SQLite Virtual File System implementation for S3

- `s3qlite.py` - The actual Lambda function that handles Athena metadata/data requests

- `sqlite_db.py` - Helper functions for access SQLite databases on S3



Create a function with the code in [lambda-function/s3qlite.py](lambda-function/s3qlite.py) that uses the previously created layer.

The handler will be `s3qlite.lambda_handler`

Also include the `vfs.py` and `sqlite_db.py` files in your Lambda function



Configure two environment variables for your lambda function:

- `TARGET_BUCKET` - The name of your S3 bucket where SQLite files live

- `TARGET_PREFIX` - The prefix (e.g. `data/sqlite`) that you uploaded the sample sqlite database to



Note that the IAM role you associate the function with will also need `s3:GetObject` and `s3:ListBucket` access to wherever your lovely SQLite databases are stored.



### Configure Athena



Follow the Athena documentation for [Connecting to a data source](https://docs.aws.amazon.com/athena/latest/ug/connect-to-a-data-source.html).

The primary thing to note here is that you need to create a workgroup named `AmazonAthenaPreviewFunctionality` and use that for your testing.

Some functionality will work in the primary workgroup, but you'll get weird errors when you try to query data.



I named my function `s3qlite` :)



### Run queries!



Here's a couple basic queries that should work:



```sql

SELECT * FROM "s3qlite"."sample_data"."records" limit 10;



SELECT COUNT(*) FROM "s3qlite"."sample_data"."records";

```



If you deploy the SAR app, the data catalog isn't registered automatically, but you can still run queries by using the special `lambda:` schema:



```sql

SELECT * FROM "lambda:s3qlite".sample_data.records LIMIT 10;

```



Where `s3qlite` is the value you provided for the `AthenaCatalogName` parameter.



## TODO



- Move these into issues :)

- Move vfs.py into it's own module

    - Maybe add write support to it someday :scream:

- Publish to SAR

- Add tests...always tests

- struct types, probably

- Don't read the entire file every time :)

- Escape column names with invalid characters

- Implement recursive listing



## Serverless App Repo



These are mostly notes I made while figuring out how to get SAR working.



Need to grant SAR access to the bucket



```shell

aws s3api put-bucket-policy --bucket  --region us-east-1 --policy '{

  "Version": "2012-10-17",

  "Statement": [

    {

      "Effect": "Allow",

      "Principal": {

        "Service":  "serverlessrepo.amazonaws.com"

      },

      "Action": "s3:GetObject",

      "Resource": "arn:aws:s3:::/*"

    }

  ]

}'

```



For publishing to the SAR, we just execute two commands



```shell

sam package --template-file athena-sqlite.yaml --s3-bucket  --output-template-file target/out.yaml

sam publish --template target/out.yaml --region us-east-1

```



If you want to deploy using CloudFormation, use this command:



```shell

sam deploy --template-file ./target/out.yaml --stack-name athena-sqlite --capabilities CAPABILITY_IAM --parameter-overrides 'DataBucket= DataPrefix=tmp/sqlite' --region us-east-1

```