The REPEATABLE READ isolation level guarantees that a transaction will see a consistent snapshot of the database. Within a single transaction, all queries will see the same data as it existed at the start of the transaction, preventing non-repeatable reads. This is the correct choice for ensuring a consistent view throughout a long transaction, as opposed to READ COMMITTED where subsequent reads can see new commits, or SERIALIZABLE which provides stricter guarantees at a higher cost but is not specifically required for this scenario.

A partial index that excludes the dominant ‘archived’ values is the most effective. It will be significantly smaller and faster to maintain than a full B-tree index, while still providing excellent performance for queries targeting the less frequent ‘active’ and ‘pending’ statuses. The WHERE clause of the queries must match the index’s predicate (e.g., `WHERE status IN (‘active’, ‘pending’)`) to use it.

The correct sequence is to first ensure all rows comply with the constraint by updating any NULL values to a sensible default in a batched or non-locking way if possible. Once the column contains no NULLs, adding the NOT NULL constraint is a fast, metadata-only operation that requires only a brief ACCESS EXCLUSIVE lock. Adding the constraint while NULLs exist, or using a check constraint first, would require a full table scan to validate all rows while holding a strong lock.

The `random_page_cost` parameter influences the planner’s estimate of the cost of non-sequential disk reads. If it’s set too high relative to `seq_page_cost`, the planner will overestimate the cost of index scans and favor Nested Loops, which relies heavily on repeated index lookups. Lowering `random_page_cost` (especially on SSDs where random access is much cheaper) makes Hash and Merge Joins, which often involve sequential scans and sorting, more attractive to the planner.

Range partitioning on the timestamp column (e.g., by month) allows old data to be efficiently archived or removed and enables partition pruning so queries only scan relevant time slices. A BRIN (Block Range INdex) is ideal for very large, naturally ordered tables as it stores min/max summaries per block of data, making it small and fast for time-range queries on the appended data. This combination is a standard best practice for time-series data.

Gaps in sequence-generated values are normal, expected behavior in PostgreSQL. To ensure high performance, sequence values are pre-allocated and cached per session or transaction. If a transaction that obtained a sequence value rolls back, that value is lost forever, creating a gap. This is a design trade-off for performance and concurrency, not a bug. While GENERATED AS IDENTITY is the modern standard, it uses the same underlying sequence mechanism and has the same behavior regarding gaps.

For a bulk UPDATE that touches every row, maintaining indexes is a major overhead. Each updated row requires an update in every index on the table. Dropping indexes before the operation removes this cost entirely. The time saved during the UPDATE itself typically far outweighs the time to rebuild the indexes afterward with CREATE INDEX, which is a highly optimized operation. The other options would have negligible or no impact on this specific scenario.

A CHECK constraint cannot contain subqueries that reference other tables. A FOREIGN KEY enforces referential integrity, not data consistency. A stored procedure can enforce the rule but is not a declarative constraint and can be bypassed. A BEFORE INSERT OR UPDATE trigger on the `invoices` table is the correct solution. The trigger function can query the `line_items` table, calculate the sum, and compare it to `NEW.total_amount`, raising an exception if they don’t match, thereby enforcing the rule within the transaction.

PostgreSQL’s built-in logical replication replicates data changes (INSERT, UPDATE, DELETE) for subscribed tables. It does NOT automatically replicate Schema (DDL) changes such as CREATE/ALTER/DROP TABLE. The schema must be created manually on the subscriber before setting up the subscription, or managed via external tools. Sequence changes and index definitions are also not replicated by default (though sequence data can be, it requires special configuration).

The REPEATABLE READ isolation level guarantees that a transaction will see a consistent snapshot of the database. Within a single transaction, all queries will see the same data as it existed at the start of the transaction, preventing non-repeatable reads. This is the correct choice for ensuring a consistent view throughout a long transaction, as opposed to READ COMMITTED where subsequent reads can see new commits, or SERIALIZABLE which provides stricter guarantees at a higher cost but is not specifically required for this scenario.0