# sqlite-parquet-vtable

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A SQLite [virtual table](https://sqlite.org/vtab.html) extension to expose Parquet files as SQL tables. You may also find [csv2parquet](https://github.com/cldellow/csv2parquet/) useful.

This [blog post](https://cldellow.com/2018/06/22/sqlite-parquet-vtable.html) provides some context on why you might use this.

## For Linux

### Installing

#### Download

You can fetch a version built for Ubuntu 16.04 at https://s3.amazonaws.com/cldellow/public/libparquet/libparquet.so.xz

#### Building

```
./make-linux
```

The first run will git clone a bunch of libraries, patch them to be statically linkable and build them.

Subsequent builds will only build the parquet virtual table extension.

#### Building (release)

Run `./make-linux-pgo` to build an instrumented binary, run tests to collect real-life usage samples, then build an optimized binary. PGO seems to give a 5-10% reduction in query times.

#### Tests

Run:

```
tests/create-queries-from-templates
tests/test-all
```


## Use

```
$ sqlite/sqlite3
sqlite> .load build/linux/libparquet
sqlite> CREATE VIRTUAL TABLE demo USING parquet('parquet-generator/99-rows-1.parquet');
sqlite> SELECT * FROM demo;
...if all goes well, you'll see data here!...
```

Note: if you get an error like:

```
sqlite> .load build/linux/libparquet
Error: parquet/libparquet.so: wrong ELF class: ELFCLASS64
```

You have the 32-bit SQLite installed. To fix this, do:

```
sudo apt-get remove --purge sqlite3
sudo apt-get install sqlite3:amd64
```

## For Windows

The following steps were performed on Windows 10 x64 system.  

### Build

#### 1 Apache-arrow build

Configure the environment and build Apache-arrow as follows:  

https://github.com/apache/arrow/blob/apache-arrow-0.9.0/cpp/apidoc/Windows.md   

Once the build is complete, files such as arrow.lib, arrow.dll, and so on are generated.   

#### 2 Parquet-cpp build

Configure the environment and build Parquet-cpp as follows:  

https://github.com/apache/parquet-cpp/blob/apache-parquet-cpp-1.4.0/docs/Windows.md  

The version of boost-cpp can be specified as 1.66.0 to avoid version compatibility issues. Once the build is complete, files such as parquet.lib, parquet.dll, and so on are generated.  

#### 3 Sqlite3 build

1 Download and extract the following three packages into the same folder.  
sqlite-amalgamation-3490100.zip  
sqlite-dll-win-x64-3490100.zip  
sqlite-autoconf-3490100.tar.gz  
2 Open the developer command prompt for VS 2017, switch to the above folder, and run the following command:   

`lib /DEF:sqlite3.def /OUT:sqlite3.lib `

After the command is executed, sqlite3.lib was generated.  

#### 4 sqlite-parquet-vtable (windows) build

1 Open the parquet directory of sqlite-parquet-vtable as dll in VS2017.  
2 Configure the paths for dll, lib, and header files in VS2017.  
3 Modify all the “constexpr” in type.h in the source code of arrow to “const”.  
4 Build this project, if successful, will generate sqlite-parquet-vtable.lib and sqlite-parquet-vtable.dll.  

### Use

1 Create a new directory{your-directory}   
2  Copy the generated arrow.dll, parquet.dll, sqlite-parquet-vtable.dll from steps 1-4 to {your directory}, and also copy all dlls from C:\local\boost_1_66_0\lib64-msvc-14.1（Your actual boost installation path.） to {your directory}.  

```
$ sqlite\sqlite3.exe
sqlite> .load sqlite-parquet-vtable.dll
sqlite> CREATE VIRTUAL TABLE demo USING parquet('parquet-generator/99-rows-1.parquet');
sqlite> SELECT * FROM demo;
...if all goes well, you'll see data here!...
```

## Supported features

### Row group filtering

Row group filtering is supported for strings and numerics so long as the SQLite
type matches the Parquet type.

e.g. if you have a column `foo` that is an INT32, this query will skip row groups whose
statistics prove that it does not contain relevant rows:

```
SELECT * FROM tbl WHERE foo = 123;
```

but this query will devolve to a table scan:

```
SELECT * FROM tbl WHERE foo = '123';
```

This is laziness on my part and could be fixed without too much effort.

### Row filtering

For common constraints, the row is checked to see if it satisfies the query's
constraints before returning control to SQLite's virtual machine. This minimizes
the number of allocations performed when many rows are filtered out by
the user's criteria.

### Memoized slices

Individual clauses are mapped to the row groups they match.

eg going on row group statistics, which store minimum and maximum values, a clause
like `WHERE city = 'Dawson Creek'` may match 80% of row groups.

In reality, it may only be present in one or two row groups.

This is recorded in a shadow table so future queries that contain that clause
can read only the necessary row groups.

### Types

These Parquet types are supported:

* INT96 timestamps (exposed as milliseconds since the epoch)
* INT8/INT16/INT32/INT64
* UTF8 strings
* BOOLEAN
* FLOAT
* DOUBLE
* Variable- and fixed-length byte arrays

These are not currently supported:

* UINT8/UINT16/UINT32/UINT64
* DECIMAL