Elasticsearch Developer Interview Questions (2025)

B-tree indexes accelerate equality and range lookups; hash indexes are for equality only. Composite indexes should match query filter and sort column order. Partial indexes cover a subset of rows and are smaller. Covering indexes include all columns a query needs so the engine never touches the heap. Always verify with EXPLAIN/EXPLAIN ANALYZE and watch write overhead — every index slows inserts and updates.

Atomicity (all-or-nothing), Consistency (valid state before and after), Isolation (concurrent transactions don't interfere), and Durability (committed data survives crashes). Relational databases like PostgreSQL provide full ACID by default. Elasticsearch may trade isolation (read committed vs serialisable) for throughput, or trade strong durability (fsync) for write speed under controlled conditions.

EXPLAIN (or EXPLAIN ANALYZE to actually run it) shows the execution tree, row estimates, and cost. Look for sequential scans on large tables where an index scan was expected, high row estimate errors (stats may be stale — run ANALYZE), nested loop joins on large sets, and sort operations that spill to disk. Fix the highest-cost node first.

Normalise (3NF) to eliminate data duplication and ensure write integrity — ideal for OLTP workloads with frequent updates. Denormalise selectively for read-heavy or analytical workloads where joins are expensive: pre-aggregate, store computed columns, or use materialised views. Document every denormalisation decision and the sync strategy to keep derived data fresh.

Primary-replica replication streams a write-ahead log (or change feed) from the primary to one or more replicas. Synchronous replication guarantees zero RPO but increases write latency; asynchronous is faster but can lose committed writes on failover. For HA, use automatic failover (Patroni, RDS Multi-AZ) so a replica is promoted within seconds if the primary fails.

Take periodic base backups (daily or weekly) plus continuous WAL/binlog archiving to object storage. This enables point-in-time recovery to any second within the retention window. Test restores regularly — a backup you haven't tested is not a backup. Store backups off-site and encrypt them at rest. Document the restore runbook and target RTO.

Relational models enforce schema, support arbitrary joins, and provide strong consistency — best when data relationships are complex and correctness is paramount. Document models like MongoDB are flexible and horizontally scalable but lack join support and multi-document transaction maturity. Elasticsearch's design reflects these trade-offs; choose based on your access patterns, not hype.

Each database connection consumes memory and file descriptors. Without pooling, a spike in app instances overwhelms the database with connections. A pool (PgBouncer, HikariCP, the driver's built-in pool) maintains a fixed set of connections and queues requests. Set pool size to roughly CPU cores × 2 and monitor pool exhaustion as a leading indicator of database saturation.

Choose a shard key with high cardinality and even distribution — avoid hot shards. Range sharding allows efficient range queries but can create hotspots. Hash sharding distributes evenly but makes range queries cross-shard. Maintain a routing layer that maps keys to shards. Plan resharding before you need it — it's disruptive. Application-level sharding gives more control than database-native solutions.

Transactions group operations into an atomic unit. Deadlocks occur when two transactions each hold a lock the other needs. Prevention strategies: always acquire locks in a consistent order, keep transactions short, use SELECT FOR UPDATE SKIP LOCKED for queue patterns, and set a lock_timeout to avoid indefinite waits. Monitor pg_locks or the equivalent to detect lock contention early.

Most placements start within 48 hours. After you submit your requirement, we send 2–3 pre-screened Elasticsearch developer profiles within 24 hours. Once you select a candidate and sign the NDA, we handle onboarding and the developer can begin the same day.

Every Elasticsearch developer goes through a technical screening interview, a live coding exercise specific to Elasticsearch challenges, and a structured communication assessment. We also review their portfolio of shipped work and verify references where available. Only the top 8% of applicants pass.

We offer a 90-day replacement guarantee. If the match isn't working for any reason, your dedicated account manager will find a replacement at no extra cost and manage the transition to minimise disruption to your project.

Absolutely. We encourage it. After we send profiles, you conduct your own technical interview with each candidate. There is no commitment until you choose someone and sign the agreement. We can also arrange a paid 3-day trial task if you want to see the developer work on a real slice of your project.

We offer four models: Dedicated developer (full-time, monthly — ideal for ongoing product work), Fixed-price project (scoped deliverable with a defined budget), Hourly (minimum 10 hours — great for audits or advisory), and Hire a Team (multiple developers under one managed engagement). Your account manager will recommend the right fit based on your timeline and budget.