What methods can be used to increase the query speed to a table that processes about 100,000 new records daily, and how will this affect weekly batch processing?

To improve query speed for a table with intensive writes (about 100,000 new records per day), the following methods can be applied:

1. Indexing — creating indexes on frequently queried fields. This speeds up retrievals but may slow down inserts, so it's important to choose indexes wisely.

2. Table partitioning — dividing the table into logical parts (e.g., by date). This allows queries to scan only the necessary partitions, significantly speeding up retrievals, especially for weekly data.

3. Materialized views — precomputed aggregates or queries that are refreshed on a schedule, reducing query load.

4. Query optimization — using efficient SQL constructs, avoiding SELECT *, proper use of JOIN and WHERE.

5. Caching — for example, caching weekly query results in memory or external caches (Redis, Memcached).

6. Vertical and horizontal scaling — increasing server resources or distributing load across multiple servers.

7. Asynchronous processing and batching of records — grouping inserts to reduce overhead.

Impact on weekly queries:

• Partitioning allows quick selection of data for a specific period without scanning the entire table.
• Indexes accelerate filtering and sorting.
• Materialized views and caching reduce response time and database load.

Example of date partitioning (PostgreSQL):

CREATE TABLE records (
    id SERIAL PRIMARY KEY,
    data TEXT,
    created_at DATE NOT NULL
) PARTITION BY RANGE (created_at);

CREATE TABLE records_2024_01 PARTITION OF records
    FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

CREATE TABLE records_2024_02 PARTITION OF records
    FOR VALUES FROM ('2024-02-01') TO ('2024-03-01');

This way, queries for a specific week will only access the relevant partitions, greatly speeding up retrievals.