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You can view it by adding `?type=summary` to the URL. # Database Performance Analysis [GitHub](https://github.com/alvariitoSW/comparacion_bbdd) **Date:** October 2024 **Collaborators:** [Álvaro Martínez Gamo](https://alvariitosw.github.io/portfolio_personal/) **Technologies:** Python, MongoDB, PostgreSQL, SQLite, DuckDB --- ## Project Overview This project focuses on the architectural evaluation of four distinct database management systems: **MongoDB, PostgreSQL, SQLite, and DuckDB**. The objective was to build a standardized benchmarking suite that measures performance across CRUD operations, specifically analyzing how data volume, batching strategies, and connection management impact hardware resource utilization (CPU and RAM). ## 1. High-Fidelity Synthetic Data Generation The benchmarking process began with the development of a robust data generation engine designed to create realistic datasets for two primary entities: **Users** and **Vehicles**. To ensure a high degree of realism, a custom provider system was built on top of the `Faker` library. ### Specialized Data Providers - **Identity & Location**: Implementation of the DNI (Spanish National ID) checksum algorithm to generate valid identifiers. Location data was curated from a real dataset of Spanish postal codes, linking cities to their specific provinces and generating landline numbers with correct geographic prefixes. - **Vehicle Logistics**: A custom manufacturer-model hierarchy was developed, alongside two different Spanish license plate formats (the current "0000 AAA" and the historical province-based system). The system also calculated the "registration period" based on realistic plate age data. - **Normalization Tools**: Integrated `unidecode` to generate valid email addresses by stripping accents/special characters from names, following common corporate patterns. ### Multi-threaded Performance To scale the dataset to millions of records, the generator utilized Python’s `multiprocessing` module. - **Collision Avoidance**: To ensure uniqueness without inter-thread communication overhead, DNI and License Plate intervals were segmented and assigned to specific processes. - **Output Optimization**: Used `ujson` and direct `csv.DictWriter` streams with file locks to minimize serialization bottlenecks during the transition from memory to disk. ## 2. Standardized Benchmarking Architecture A significant challenge was creating a fair environment to compare disparate engines like a document-based NoSQL (MongoDB) and a columnar OLAP database (DuckDB). This was solved using an **Object-Oriented Design Pattern**. ### The Abstract DB Interface A base class `DB` was implemented to define a uniform interface for all operations: - **Connection Management**: Logic to toggle between `Keep-Alive` (persistent connection) and `Close-on-Op` (opening/closing for every transaction) to measure the impact of connection overhead. - **Batching Engine**: Configurable batch sizes for `INSERT` operations, allowing the study of how write-ahead logs (WAL) and memory buffers behave under different pressures. - **Caching Layer**: Integrated `pymemcache` as an external caching layer to compare native query performance against memory-object caching. ### Database Engine Managers - **PostgreSQL**: Implemented via `SQLAlchemy` and `psycopg2` to manage remote server interactions. - **MongoDB**: Utilized `pymongo` with bulk-write operations for high-concurrency simulation. - **SQLite/DuckDB**: Leveraged native drivers for local file-based and in-memory performance comparisons. ## 3. Technical Execution and Resource Monitoring The project prioritized objective hardware measurement over simple execution time. ### Performance Instrumentation The system tracked three primary metrics per operation: 1. **Real Time (Latency)**: Calculated using `time.perf_counter` to capture the end-user experience. ![Real time taken to complete the operations. Y Axis is the time in seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/real-time.webp) 2. **CPU Time (Throughput)**: Measured via `time.process_time` to evaluate effective computational work versus IDLE wait states (I/O). ![Real time taken to complete the operations. Y Axis is the time in CPU seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/cpu-time.webp) 3. **Memory Profiling**: Integrated `memory_profiler` to measure the average and peak RAM consumption during massive inserts and full table scans. ![Memory usage during the operations. Y Axis is the usage in MB and X Axis is the size of the dataset.](../../assets/projects/db-performance/memory.webp) ### Operation Profiles The benchmark executed four distinct test scenarios across datasets of $10^3, 10^4,$ and $10^5$ records: - **Bulk Insert**: Comparing the overhead of relational schema enforcement vs. JSON document flexibility. ![Real time taken in Bulk Insert measurements. Y Axis is the time in seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/insert.webp) - **Indexed Search**: Evaluating B-tree efficiency in relational engines vs. MongoDB's indexing performance on specific fields like DNI or VIN. ![Real time taken in Indexed Search measurements. Y Axis is the time in seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/read.webp) - **Complex Joins**: Measuring the performance of relational `JOIN` operations against MongoDB’s `$lookup` aggregation pipeline. ![Real time taken in Complex Joins measurements. Y Axis is the time in seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/join.webp) - **Massive Updates**: Analyzing the "In-place" update behavior versus row/column rewriting. ![Real time taken in Massive Updates measurements. Y Axis is the time in seconds and X Axis is the size of the dataset.](../../assets/projects/db-performance/update.webp) ## 4. Key Implementation Details - **Type Mapping**: A custom dictionary was implemented to map Pandas data types to specific SQL types across different dialects (e.g., mapping `int64` to `INTEGER` in Postgres and `REAL` in SQLite). - **Index Management**: Automatic creation of unique indexes and secondary indexes after data insertion to isolate the cost of index maintenance during writes. - **DuckDB Persistence**: Specific handling for DuckDB’s columnar format, involving table deletion and recreation for clean-state testing.