Microservice for calculating game statistics and match analytics

Budget: 2000 EUR Deadline: 7 days

Hello, I worked on an analytical service for an esports platform where we processed over 2 million game events per day using Pandas without for loops, and reduced the metric calculation time to less than 3 seconds ⚡

The project is very similar to yours: we also needed to aggregate raw logs (kills, deaths, damage) and provide structured analytics through FastAPI + Pydantic validation.

A question regarding the essence: do you plan to process events in real-time (streaming from a queue like Kafka), or is batch processing of completed session logs sufficient? This affects the choice between ClickHouse and PostgreSQL.

I suggest we get in touch; I will provide you with free technical consultation and we can outline a development plan + I will tell you about my team!

Budget: 333 EUR Deadline: 10 days

Ivan, you need a service for quick processing of game logs that translates "raw" events into understandable metrics of player performance. The main challenge here is not just to collect data, but to properly vectorize operations so that the service does not choke when analyzing large arrays of matches. I will build the processing architecture using a combination of Pandas and NumPy, completely eliminating loops in calculations, which will ensure high performance during event aggregation. We will validate all incoming load through Pydantic, and for quick deployment, I will package the service and the chosen database in Docker. Please tell me what the average volume of logs per match is expected to be and if there is already a ready JSON schema that the service should accept as input?

Budget: 1000 EUR Deadline: 7 days

Hello!

I am interested in your project. I have experience in developing high-load backend services in Python, working with FastAPI, PostgreSQL, Docker, as well as processing large volumes of data with a focus on performance.

I can implement a service that:

* accepts and validates game events via FastAPI and Pydantic;
* efficiently aggregates large data sets using Pandas/NumPy without using `for` loops where it affects performance;
* calculates necessary player metrics (K/D, damage, efficiency, goal completion, and other indicators);
* supports data storage in PostgreSQL or ClickHouse (if really large volumes of logs are expected);
* provides a convenient API for obtaining analytics;
* is fully deployed via Docker Compose.

In development, I pay attention not only to the correctness of calculations but also to the scalability of the service. The architecture will be designed in such a way that new types of game events, metrics, and data sources can be added in the future without significant system redesign.

I would be happy to discuss the structure of incoming events, expected data volumes, and the set of calculated metrics to propose an optimal architecture.

Best regards,
Dmitry Expand

Budget: 25 EUR Deadline: 1 day

Hi Yvan,

This fits work I do regularly, FastAPI services processing structured event data with Pandas for the heavy aggregation and Pydantic for schema validation on the way in.

For the metrics calculation, the no-for-loops requirement makes sense given the volume you're describing, vectorized Pandas/NumPy operations over the raw event arrays (kills, deaths, damage, missions) rather than iterating row by row, groupby and aggregate at the DataFrame level, push down to ClickHouse directly when the dataset gets large enough that pulling everything into memory stops making sense.

A few things that would help me scope this accurately. What's the expected volume, are we talking thousands of events per match or millions across a live pipeline, since that decides whether ClickHouse is worth the setup cost over just PostgreSQL with good indexing. Do you already have a sample of the raw log format, even a few real records would let me nail the Pydantic schema on the first pass instead of guessing at field names. And is this a one-time ingestion service or does it need to handle continuous incoming sessions, that changes whether I build in a queue or keep it simple request-response.

I'd set it up as FastAPI with Pydantic models validating incoming payloads, a Pandas-based aggregation layer for the metrics computation, dockerized alongside whichever database fits your volume, with docker compose so the whole thing runs with one command on your end.

Send over a sample payload if you have one and I'll come back with a firm price and timeline instead of a rough estimate. Expand

Budget: 450 EUR Deadline: 9 days

Hello! I am ready to develop a microservice for calculating game statistics using FastAPI and Pandas. I will ensure high performance and quality analytics. Let's discuss the details!

Budget: 50 EUR Deadline: 2 days

Hello. The project for creating a gaming session analytics service is well understood and interesting for me to implement. The architecture of the service will be built on FastAPI with Pydantic for reliable input data validation, and for fast aggregation of metrics on large volumes of data, I will use vectorized operations from Pandas and NumPy, avoiding for loops as specified in the requirements. The use of ClickHouse will ensure optimal performance for analytical queries, and Docker containerization will guarantee easy deployment and scaling of the solution. I already have ready architectural templates and optimized scripts for similar tasks, which will significantly speed up the development process and increase reliability. I suggest discussing all implementation details, final budget, and timelines in private messages.

Budget: 348 EUR Deadline: 5 days

Hello! I am interested in your task of creating an analytical microservice. I have excellent experience working with FastAPI and Pydantic for data validation, and I am also deeply familiar with Pandas and NumPy for vectorized computations, which is critically important for processing game logs without using slow loops. I am ready to implement an efficient architecture using ClickHouse or PostgreSQL, package everything in Docker, and ensure high data processing performance. I am ready to discuss the details and start working, feel free to write.