Metadata-Version: 2.1
Name: grizzly-sql
Version: 0.1.2
Summary: A Python-to-SQL transpiler to work with relational databases
Home-page: https://github.com/dbis-ilm/grizzly
Author: Databases & Information Systems Group, TU Ilmenau
Author-email: stefan.hagedorn@tu-ilmenau.de
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# Grizzly

[![Testing](https://dbgit.prakinf.tu-ilmenau.de/code/grizzly/badges/master/pipeline.svg)](https://dbgit.prakinf.tu-ilmenau.de/code/grizzly/commits/master)
[![coverage report](https://dbgit.prakinf.tu-ilmenau.de/code/grizzly/badges/master/coverage.svg)](https://dbgit.prakinf.tu-ilmenau.de/code/grizzly/commits/master)

Grizzly is a transpiler from a Python-API to SQL to move computations from the client into a database system.

Grizzly implements its own `DataFrame` structure that tracks operations, like projection, filter, joins, ...
Only when the result of the sequence of operations is needed, a SQL string is produced, resembling all those operations, and sent to a DBMS.
This way, you don't have to care about Out-of-Memory problems, un-optimized queries, and high CPU load.

## Installation

Grizzly is available on PyPi: <https://pypi.org/project/grizzly-sql>

```python
pip3 install --user grizzly-sql
```

## Dependencies

Grizzly uses

- Python 3
- [SQLite3](https://docs.python.org/2/library/sqlite3.html) (currently for tests only)
- [BeautifulTable](https://github.com/pri22296/beautifultable) for pretty output

## Getting started

### Import

As with any Python module, just import it

```Python
import grizzly
```

### Connection

Connect to your database using an appropriate connection string:

```python
import sqlite3
con = sqlite3.connect("grizzly.db")
```

Now, reference the table(s) you want to work with:

```python
df = grizzly.read_table("events", con)
```

Here, `df` is just a reference, it contains no data from your table.
To show its contents, use the `show` method:

```python
df.show()
```

This will print the table's content on the screen.

### Filter & Projection

Operations are similar to Pandas:

```python
df[df["id" == 42]] # filter
df = df[["actor1","actor2"]]
```

### Joins

A `DataFrame` can be joined with another `DataFrame`:

```python
df1 = grizzly.read_table("table1")
df2 = grizzly.read_table("table2")

joined = df1.join(df2, on=["joinCol1", "joinCol2"], how="inner", comp='=')
```

In the `on` parameter, you specify the join columns. The first one is for the left input (`df1`), the second one for the right input (`df2`).
The `how` parameter is used to select the join type: `inner`, `left outer`, etc. This value is directly placed into the generated query, and thus depends on
the dialect of the underlying DBMS. An additional `comp` parameter lets you choose the comparison operator.

You sometimes want to join on multiple columns with different comparisons. For this, in Grizzly you define the expression as if it was for filters:

```python
df1 = grizzly.read_table("t1")
df2 = grizzly.read_table("t2")

j = df1.join(df2, on = (df1['a'] == df2['b']) & (df1['c'] <= df2['d']), how="left outer")
```

This results in the following SQL code:

```sql
SELECT  *
FROM t1  
    LEFT OUTER JOIN (SELECT  * FROM t2   ) IOBRD
    ON (t1.a = IOBRD.b) AND (t1.c <= IOBRD.d)
```

Here, `IOBRD` is an autogenerated tuple variable needed to avoid disambiguation.

### Grouping & Aggregation

You can also group the data on multiple columns and compute an aggregate over the groups:

```python
df = grizzly.read_table("events")
df = df[df['id'] == 42]
g = df.groupby(["year","actor1"])

a = g.count("actor2")
```

If no aggregation function is used an `show()` is called, only the grouping columns are selected.
You can apply aggregation functions on non-grouped `DataFrame`s of course. In this case the aggregates will be computed for the whole content.

Thus, `a.sql()` will give

```sql
SELECT year, actor1, count(actor2)
FROM events
WHERE id = 42
GROUP BY year, actor1
```

, whereas `df.count()` (i.e. before the grouping) for the above piece of code will return the single scalar value with the number of records in `df`:

```sql
SELECT count(*)
FROM events
WHERE id = 42
```

### SQL

You can inspect the produced SQL string with `sql()`:

```python
print(df.sql())
```

And the output will be

```sql
SELECT actor1, actor2
FROM events
WHERE id = 42
```

## Supported operations

- filter/selection
- projection
- join
- group by
- aggregation functions: min, max, mean (avg), count, sum


