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What a database index is and when it helps
An index is a sorted lookup structure avoiding full scans, helps selective WHERE/JOIN columns, but costs write overhead.
Composite index column order for multi-column filters
Create one index on (last_name, first_name); order matters because the index serves leftmost-prefix lookups.
Clustered versus non-clustered indexes
A clustered index orders the table's actual rows, one per table; a non-clustered index is a separate structure pointing to rows.
What is a query execution plan?
The plan is the optimizer's chosen tree of operators; run EXPLAIN or EXPLAIN ANALYZE; watch for sequential scans, bad row estimates, and costly joins.
Trade-offs of adding indexes to a table
Indexes speed reads but slow writes since every INSERT, UPDATE, and DELETE must maintain them; they consume storage and can be unused on low-selectivity columns.
What is a covering index?
A covering index contains every column a query needs so the engine answers from the index alone, skipping the table heap; build it by including filter, join, and selected columns.
B-Tree versus Hash indexes
B-Trees keep keys sorted, supporting equality, range, prefix, and ORDER BY; hash indexes give O(1) equality only, no ranges or ordering.
Optimizer picks nested loop over hash join
Nested loop wins on few rows, so bad row estimates from stale stats or skewed data trick it; fix by refreshing statistics, adding histograms, rewriting predicates, or ensuring memory for hashing.
Indexing a low-cardinality status column
With three values each matches a third of rows, so the optimizer prefers a scan over costly random heap fetches; alternatives include partial indexes on rare values and composite indexes leading with status.
What is a database page?
A page is a fixed-size block, often 8KB, holding multiple rows; databases read and write whole pages because disk and OS I/O are block-oriented, amortizing seek cost and matching the buffer pool unit.
Purpose of the Write-Ahead Log
WAL records changes sequentially and is flushed to disk before commit; the rule is log first, then data pages may lag; on crash the database replays the log to recover committed work.
Database checkpoints with WAL
A checkpoint flushes dirty pages and records a known-good point so recovery can start later in the log; frequent checkpoints shorten recovery but add I/O spikes, infrequent ones lengthen…
Heap file versus clustered index
A heap stores rows unordered with cheap inserts but no inherent ordering; a clustered or index-organized table stores rows in primary-key order, giving fast key range reads but costlier inserts and page…
Lifecycle of a single row update
Buffer manager faults the page in, the row is modified in memory marking the page dirty, a WAL record is written, and commit fsyncs the log while the dirty page is flushed later by a checkpoint.
B+ Tree range queries across pages
Internal nodes are pages of keys guiding the search, all data sits in linked leaf pages; a range query descends to the start key then follows the leaf chain sequentially until the upper bound.
The three phases of ARIES recovery
Analysis rebuilds dirty-page and transaction tables from the last checkpoint, Redo replays all logged changes to restore state, Undo rolls back losers; Redo is idempotent via per-page LSN comparison so…
Row-oriented versus columnar storage
Row stores keep whole rows together, ideal for point reads and writes; columnar stores keep each column contiguous, enabling reading only needed columns and strong compression, ideal for scans and…
Using page LSN to decide redo
Each page stores the LSN of its last applied change; during redo the engine reapplies a log record only if its LSN exceeds the page's LSN, meaning the change is not yet reflected on disk.
Stages of executing a SELECT query
Parse the SQL into a tree, bind and validate against the catalog, optimize into a physical plan, then execute the plan operators fetching data and returning rows.
Why developers read query plans
The plan shows the operators the optimizer chose to run a query; developers read it to find why a query is slow; a common thing to look for is a full table scan where an index was expected.