FunQL is a federated unified query language designed to integrate various independent data sources into a seamless query experience across different database types without requiring programming knowledge. It offers benefits such as simplified data retrieval, advanced aggregation options, and faster development, while facilitating coexistence and migration between NoSQL and relational databases. The document outlines the architecture and various functionalities of FunQL, emphasizing its potential for improving data integration and business intelligence.

1. Querying MongoDB without
programming using
FUNQL

2. FUNQL?
Federated
Unified
Query Language

3. What does this mean?

Federated - Integrates different independent
stand alone data sources into one coherent
view

Unified – One query for different database
technologies: Relational, Document Oriented,
Object Oriented, Key/Value (yes its possible!)

Query Language – Domain: Filtering, data
transformation, aggregation. Can be executed
interatively. No programming knowledge
required.

4. When to Use

Ad-Hoc queries

When Map/Reduce is overkill

When you don't want to get lost in $}':“{ syntax

Data integration

Business Intelligence

Pre aggregation

5. Benefits for Mongo

Easier use of MongoDB and quicker completion of
projects.

Standardizes the heterogeneous NoSQL Market.

Protects customer investments by making MongoDB part
of a larger market.

Enlarges the pool of skilled users.

Learning environment for non-technical users and SQL
users.

Co-existence with relational databases and migration
made easy.

6. Benefits for developers

Skill applicable to different database systems.

Faster development.

Easy testing and data cleaning.

Larger market for developer skills.

Protects your investment in learning NoSQL.

Fast prototyping.

7. Benefits for developers

Skill applicable to different database systems.

Faster development.

Easy testing and data cleaning.

Larger market for developer skills.

Protects your investment in learning NoSQL.

Fast prototyping.

8. Example
from collection
where object.subobject.field = $parameter
select field, { nested, fields, … }
group by field, aggregates, sub_groups
where condition // on aggregate //= having
into … // File (json/xml) or other database/collection

9. From - Where - Select
why it makes a big difference to
SQL's select - from - where

Seemingly unimportant syntactical sugar

But it completely changes the execution model of a
query.

Simplifies it from declarative to data-flow

Each statement is a pipeline, reading data from the
previous statement and passing (modified) data to the
next.

Can easily be executed in parallel.

Not invented here: Pioneered by Microsoft with Linq

10. Dealing with relational data

The join operation fits into the same model.
from table1
join table2 on table1.id = table2.foreign_id

Is now just an pipeline stage, that adds the data from
table2 into the dataflow.

Tables can come from different systems. E.g one is
from MongoDB, the other from DB2

Join grouped operator – returns a 1-n relationship a a
collection of nested documents.

11. Nested/Recursive Data Structures

flatten operator transforms a (fixed) nested
structure into one linear table.

enumerate linearizes (walks through) an
arbitrarily deeply nested tree.

group turns a linear structure into a nested
structure.

Funql understands database references. You
can use dot-notation for navigation.

12. Aggregation

The model allows much more advanced
aggregation operations than SQL

Multi dimensional grouping

Mixing of normal select and aggregation

Analytics directly in the query

Can leverage MongoDBs new aggregation
operators

13. Funql and the Aggregation
Framework

Both use the same pipeline model

select statement == mongos project

flatten stetement ~~ mongo's unwind

range == first, last

Simple mapping, so Funql can operate at
Mongo's native speed.
db.zips.aggregate({$group:{_id: '$city', pop: {$sum: '$pop'}}}, {$sort: {pop: -1}}})
from zips
group by city, sum pop as pop
sort by pop descending

14. Status

Formal language spec at
reportsanywhere.com/pebble

Twitter: @reportsanywhere

funql.org is coming up

Reference implementation in Java currently for
 MongoDB
 Generic Json data
 Cassandra (coming)
 Relational Databases (coming)

Fork it on Bitbucket (coming up)

15. Architecture

Core Engine

Can filter, select, join, sort, group, aggregate

Extremely simple driver API: 5 Methods.
open/close, iterate, lookup, range

Not necessarily optimized

Advanced driver API: Examines the syntax tree
and invokes native operations.