Samples¶
TinyOlap comes with a few sample databases that are intended to explain the concept of TinyOlap, and how you can build or import your own data models and databases. You will find the samples in the samples folder.
1. Tiny Database¶
Usage: Open and/or run the script […tinyolap/samples/tiny.py](https://github.com/Zeutschler/tinyolap/blob/main/samples/tiny.py)
Purpose of the Tiny data model¶
The Tiny data model is a very small and simple demo database created with Python code. It’s contains just very small 5 dimensions and 1 cube that reflects some sales data. The intended use is to showcase the all the basic operations to create and access a TinyOlap database by code. It also shows how to create and print simple reports to the console with the build in Slice object.
2. Tesla Database¶
Usage: Open and/or run the script …tinyolap/samples/tesla.py
Purpose of the Tesla data model¶
The Tesla is the famous example that we use in the documentation and on the TinyOlap website . It show cases some corecapabilities of TinyOlap.
3. Enterprise Planning Database¶
Attention
Under development. Coming soon.
Usage: Open and/or run the script …tinyolap/samples/enterprise.py
Purpose of the Enterprise data model¶
The Enterprise sample is a more realistic example for financial planning, reporting and forecasting using TinyOlap. The entire database is randomly generated by Python code and realistically represents the structure and data volume for a small company group. The Finance data model could already be suitable a template to implement financial planning for your company. We tagged is as advanced as the code that generates the data model looks a bit weird and complex. The datamodel itself is still quite simple.
Before looking at any code, you should take a look at the visual result. Just run the [/samples/enterprise_web_demo.py](https://github.com/Zeutschler/tinyolap/blob/main/samples/enterprise_web_demo.py) from within your IDE or console. Data generation might take a while. Then click on the link provided on your console output.
INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
The Enterprise data model provides the following features, leveraging most of TinyOlap’s capabilities:
Sales figures by legal entities and products based on price and quantity.
Profit and Loss statement, integrating all other plans.
(coming soon) HR plan covering employees some attributes and their salaries. Segmented by legal entities.
(coming soon) Production plan covering planned quantities, raw material costs and capacities.
(coming soon) User management and access rights, so, e.g., the sales guy can look into the salary data and only the finance guy can see certain cost figures and EBIT. And the boss can see anything but is not allowed to change data.
4. Huge (the opposite of tiny)¶
Warning
Please be aware that your RAM is limited. Don’t overdo it! Python will crash when your running out of memory.
Usage: Open and/or run the script …tinyolap/samples/huge.py
Purpose of the Huge data model¶
The Huge data model is already a quite larger database, at least for TinyOlap.
It showcases how TinyOlap behaves on larger data sets.
You can play around with the parameters numbers_of_records (default = 1,000,000),
numbers_of_dimensions (default = 8) and ``members_per_dimension (default = 100) to
check how the database behaves and perform und varying circumstances.
As a rule of thumb, TinyOlap databases consume an average ±1.5kb per record (incl data model).
5. Plane Spotter¶
Usage: Open and/or run the script …tinyolap/samples/planespotter.py
Purpose of the Plane Spotter data model¶
The Plane Spotter data model is kind of a gimmick and creates a data model containing real-time flight data from the great OpenSky online network.
The data model show cases how you can update the structure and content of database in more or less real time. Although the request for flight data will need some time (sometimes seconds), the actual update and import of the data model only takes a few milliseconds.
6. Tutor - A vintage data model¶
Usage: Open and/or run the script …tinyolap/samples/tutor.py
Purpose of the Tutor data model¶
The tutor data model is a typical sales planning and reporting data model. Although being very old and special (see History below) it nicely reflects how business often was and still is structured.
The data model contains products (PRODUKTE), regions (REGIONEN), time dimensions (JAHRE, MONATE), some value types (DATENART) with actual (‘Ist’) and plan figures, and finally a small set of measures (WERTART) contain quantity (‘Menge’), sales (‘Umsatz’), cost (‘variable Kosten’) and a profit contribution (‘DB1’).
Tutor is the largest sample data model coming with TinyOlap. With exactly 135,443 records, it’s already reflects a somehow realistic data volume for the business planning of a smaller to mid-sized company. Enjoy this …
The special history of the Tutor database¶
The tutor database is a piece of OLAP history, it’s almost 30 years old, actually from the pre-internet area. The Tutor database was shipped as the sample database of MIS Alea, one of the first true MOLAP databases available. MIS Alea was developed by the MIS Group in Darmstadt, Germany. Actutally MIS Alea was a clone of TM/1, which itself was developed by Manny Perez at Sinper Corp., USA. After several company transitions, MIS Alea is still successful in the BI market and is now owned by Infor and currently called Infor d/EPM, if I’m not already changed.
The Tutor database is in german language, but it should be understandable for everyone. The TXT files in the folder tutor are the original files ship with the database on a 3½-inch disk at around 1995, they are single-byte latin-1 encoded (ISO 8859-1).
7. Tutor Web Demo¶
Usage: Open and/or run the script …tinyolap/samples/tutor_web_demo.py
Purpose of the Tutor WEb Demo¶
The tutor web demo simply spins up very rudimentary web service on top of the Tutor data model described above.
The implementation is lousy (I’m not a frontend guy) and is intended to showcase the slice feature of TinyOlap and to provide a nicer visual interface. All other samples just create console output.
8. Tiny42 - TinyOlap parallel-processing¶
Usage: Open and/or run the script …tinyolap/samples/tiny42.py
Purpose of the Tiny42 data model¶
This example show cases the cloning of databases and how to use multiprocessing (not multi-threading) to process multiple databases in parallel. The example works in-memory, in order to not flood you disk with database files.
We create 1x database template, create 42x independent clones and process them in a distributed manner, whatever them might be, e.g., recipients, machines, locations or departments). The clones get adapted (by adding some new members) and filled with some data. When anything is recollected, we consolidate all the clones into one single databases.
For illustration purposes we use an IoT sample for the Tiny Marmalade Factory, where 42x marmalade machines create some senor data. Each machines return their own machine and sensor ids.