In this final post of the series, I’ll review key concepts from the Principios de analítica en tiempo real course, covering topics I missed in my previous posts.

This is a quick review about he topics cover on the course.

./Hardware Considerations

Hardware access speed

  • Volatile storage is significantly faster than non-volatile storage.
  • Random access on non-volatile storage is much slower than sequential access. Optimize for sequential reads whenever possible.
  • In cloud environments, cached data can be evicted unexpectedly due to shared resources.

./Source Data and Ingestion

Efficient data ingestion is crucial for real-time analytics:

  • Minimize disk I/O by ingesting data in larger batches rather than individual rows.
  • When working with files, prefer multiple smaller files over one massive file for improved parallelism.
  • Use streaming-friendly compression algorithms like gz.
  • Structured data (e.g., Parquet, ORC) enables faster processing compared to unstructured or semi-structured data.
  • Data is often categorized into event tables (large, high-volume) and dimension tables (smaller, descriptive).

Ingestion Patterns

  • Upsert: Insert new data and update existing records if a match is found.
  • Full Replace: Completely replace existing data, typically used for dimension tables.
  • Partial Replace: Replace a subset of data (e.g., data for a specific month).

./Storing Data

Optimizing data storage is essential for query performance:

  • A well-defined schema ensures data homogeneity and enables efficient query planning.
  • Compression reduces data transfer time but increases CPU load for decompression. Choose an appropriate algorithm based on your workload (e.g., LZ4 for speed, ZSTD for compression ratio).
  • Use the smallest appropriate data types to minimize memory usage.
  • Sorted data can speed up queries, but the optimal sort order depends on the specific queries.
  • Indexes provide fast data access, similar to an index in a book.

./Query Optimization

Efficient querying relies on several strategies:

  • Parallelization: Distribute queries across multiple machines or CPU cores.

Parallelization query

  • Minimize Data Read: The fastest query is the one that reads no data. Use indexes, filters, and prefilters effectively.
  • Order of Operations: Perform lightweight, indexed operations first to reduce the data volume before applying more complex operations.
  • Data Type Awareness: Consider the performance implications of different data types (e.g., integer vs. string comparisons).
  • Join Strategies: While denormalization can avoid joins during ingestion, joins are often unavoidable. Choose appropriate join algorithms (e.g., merge join) and optimize join conditions.
  • Algorithm Selection: Use specialized algorithms optimized for specific tasks (e.g., uniq for approximate unique counts, uniqExact for precise counts).

./Materialized Views

Materialized views pre-compute and store query results, trading storage space for faster query performance:

  • Maintenance: Materialized views require synchronization with the underlying data.
  • Cost Savings: While increasing storage costs, materialized views can significantly reduce compute costs, resulting in overall savings.
  • Intermediate States: Materialized views can store intermediate results, especially useful in parallelized queries. New data ingested adds new intermediate states. When querying, specify to merge these intermediate states.

./Exposing the Data

Tinybird typically exposes data through APIs. Implement robust data testing and benchmarking:

  • Data Quality Checks: Verify data integrity by checking row counts, key aggregations, and missing values. Automate these tests as part of your data pipeline..
  • Benchmarking: Create realistic benchmarks that mimic expected user load.

./Scaling

Scaling distributed systems introduces significant complexity:

  • Sharding and Replication: Sharding distributes data across multiple machines for parallel processing, while replication provides redundancy and high availability, also speed.
  • Resource Allocation: Dedicate separate resources for ingestion, processing, and querying to maximize performance and avoid resource contention. For example, separate the ingestion workload (ordering, compression) from the query-serving databases.