Database Optimization Services · 24 Supported Database Technologies
Database Performance Optimization & Tuning Services Across Multi-Database Stacks
What Do Database Optimization Services Include and How Does JusDB Tune Production Workloads?
Database performance optimization services systematically isolate and eliminate slow queries, lock contention, index bloat, and connection pool saturation across production databases. JusDB combines engine-specific EXPLAIN plan forensics, online covering-index design, PgBouncer/ProxySQL pooling, and memory/IOPS sizing across the 24 database technologies we support — baselining workload metrics before and measuring after every approved change.
Initial-response targets are 2 to 4 hours during covered business hours, Mon–Fri 09:00–18:00 IST (UTC+5:30). Where your service agreement includes 24x7 on-call, the initial-response target for P1 incidents is 15 minutes. Targets are not resolution guarantees.
JusDB applies a structured 6-phase database performance audit to any production engine — MySQL, PostgreSQL, MongoDB, SQL Server, MariaDB, Cassandra, Aerospike, ClickHouse, StarRocks, and Redis. Query profiling, index architecture review, schema normalization, connection pool tuning, and storage IOPS sizing: one methodology, executed with deep engine-specific internals.
Need engine-specific database performance tuning? Jump directly to MySQL Performance Tuning, PostgreSQL Performance Tuning, MongoDB Performance Tuning, SQL Server Performance Tuning, MariaDB Performance Tuning, or try our free MySQL Optimizer and PostgreSQL Optimizer tools.
What Every Audit Measures
Results depend on the workload; we baseline before and measure after each approved change.
The 6-Phase Database Performance Audit
Most "performance fixes" are guesswork: add an index, bump the memory, hope it gets faster. JusDB uses a structured methodology that starts with measurement, not assumptions — identifying the highest-impact changes before touching anything.
Workload Capture
We capture your real query workload using slow query logs, pg_stat_statements, MongoDB Atlas profiler, or Cassandra tracing. We identify the top 20 queries by total wall time — the ones where optimisation will have the largest impact.
Query Profiling & EXPLAIN
For each high-cost query, we run EXPLAIN ANALYZE (or engine equivalent), identify full table scans, type conversions, sort spillovers, and bad join orders. We produce a prioritised list of query rewrites and index changes.
Index Architecture Review
We audit your entire index set: missing indexes on frequently filtered columns, redundant indexes consuming write I/O, wrong index types (B-tree vs GIN vs partial). Index bloat causes slower reads and heavier writes — we fix both.
Schema & Data Model Review
The schema is often where performance debt hides. Wide rows, unbounded TEXT columns, missing foreign key indexes, N+1 query patterns from ORM misuse — we surface and fix the root cause, not just the symptom.
Connection & Resource Tuning
Connection pool exhaustion causes latency spikes that look like slow queries. We tune max_connections, pool sizes (PgBouncer, ProxySQL, Mongos), buffer pool sizes, shared_buffers, and InnoDB settings to match your actual workload.
Hardware & Storage Sizing
A query that needs 200MB of working set will be fast if that fits in buffer pool — and 100x slower if it hits disk. We model your dataset, hot set size, and I/O profile to validate whether your current instance type and storage are correctly sized.
Engine-Specific Database Performance Tuning
The same systematic audit process, applied with engine-specific depth. JusDB applies query, index, and storage forensics across the 24 database technologies we support:
MySQL Performance Tuning
InnoDB buffer pool sizing, Performance Schema digests, redo log capacity, ProxySQL pooling, and online index builds
PostgreSQL Performance Tuning
EXPLAIN (ANALYZE, BUFFERS), pg_stat_statements, autovacuum cost tuning, BRIN/GIN/partial indexes, and PgBouncer
MongoDB Performance Tuning
Atlas Profiler, ESR compound index rule, aggregation pipeline $lookup/$sort optimization, and WiredTiger cache sizing
SQL Server Performance Tuning
Query Store plan forcing, wait-stats forensics (CXPACKET, PAGEIOLATCH), TempDB contention, and columnstore indexing
MariaDB Performance Tuning
InnoDB & Aria buffer tuning, thread pool concurrency, Galera flow-control diagnostics, and MaxScale query routing
Cassandra Performance Tuning
SSTable compaction strategy (STCS/LCS/TWCS), tombstone eradication, partition sizing, and off-heap chunk cache tuning
Aerospike Performance Tuning
Hybrid Memory Architecture (HMA) sizing, primary/secondary index tuning, batch read optimization, and NVMe device layout
ClickHouse Performance Tuning
MergeTree ORDER BY key selection, data skipping indexes, vectorized memory limits, and materialized view pre-aggregation
StarRocks Performance Tuning
Colocate and bucket shuffle joins, CBO statistics collection, data cache sizing, and synchronous/async materialized views
Redis Performance Tuning
Memory fragmentation ratio tuning, big-key scans, Lua script & pipeline batching, and eviction policy calibration
Free Engineering Calculators
Interactive Database Configuration & Pool Sizing Tools
Generate baseline memory, buffer pool, redo/WAL, and connection pool configurations before scheduling a deep query and index audit:
MySQL Configuration Optimizer
Generate workload-tuned my.cnf parameters (innodb_buffer_pool_size, innodb_redo_log_capacity, thread_cache_size) for your RAM and vCPU profile.
PostgreSQL Configuration Optimizer
Calculate production postgresql.conf settings (shared_buffers, work_mem, effective_cache_size, autovacuum workers) for OLTP or analytics.
PgBouncer Pool Sizing Calculator
Model transaction-mode pool_size, max_client_conn, and reserve_pool_size to prevent PostgreSQL connection exhaustion.
ProxySQL Configuration Generator
Build MySQL read/write split rules, connection multiplexing thresholds, and hostgroup health checks.
Emergency Performance Triage & Severity Initial-Response Targets (P1–P4)
Business-Hours Baseline: 2 to 4 hours (During covered business hours)| Severity | Initial-Response Target | Performance / Outage Trigger | Coverage Window |
|---|---|---|---|
| P1 · Critical | 15 minutes | Production database down, primary unreachable, data corruption risk, or active failover event. | 24x7 on-call (under your service agreement) |
| P2 · High | 30 minutes | Severe latency spike, replication lag climbing rapidly, connection exhaustion, or degraded HA node. | 24x7 on-call (under your service agreement) |
| P3 · Medium | 60 minutes | Non-critical slow query regression, staging issue, backup warning, or replication check alert. | 24x7 on-call (under your service agreement) |
| P4 · Low | 90 minutes | Configuration question, index advisory, planned schema review, or general operational request. | 24x7 on-call (under your service agreement) |
What You Receive from a Performance Audit
Query Performance Report
Top 20 queries by total cost, with EXPLAIN output, proposed rewrites, and expected improvement estimate for each.
Index Audit
Full index inventory: missing indexes, redundant indexes (with I/O cost), bloated indexes, and wrong index types. Ordered by impact.
Configuration Recommendations
Engine parameter changes with rationale: buffer pool size, max_connections, autovacuum settings, work_mem — tailored to your hardware and workload.
Schema Improvement Plan
Specific schema changes: column type corrections, partition strategy, normalisation opportunities, and ORM-generated N+1 queries to eliminate.
What makes JusDB different
- We audit any database — you don't need to engage 4 different consultants for your 4 databases
- We start with your worst queries, not a checkbox compliance report
- Every recommendation is quantified: expected latency improvement, I/O reduction, or cost saving
- We implement the fixes, not just deliver a document — optional hands-on implementation engagement
- We validate improvements before closing the engagement: before/after benchmarks, not just EXPLAIN estimates
- We leave behind runbooks for the changes we made so your team understands the rationale
Complex Performance Bottlenecks We Routinely Resolve
When production database latency suddenly spikes, generic cloud charts rarely pinpoint the root cause. Here are complex failure states our tuning directly resolves:
PostgreSQL Autovacuum Starvation & Silent Index Bloat
High-churn tables accumulate millions of dead tuples because default autovacuum workers cannot keep pace with write volume. B-tree indexes bloat to multiples of actual table size, forcing slow random disk reads.
We tune per-table autovacuum_vacuum_cost_limit and autovacuum_vacuum_scale_factor, then rebuild bloated indexes online using non-blocking pg_repack runbooks.
MySQL InnoDB Buffer Pool Choke & Redo Checkpoint Stalls
Sudden bursts of write traffic fill the InnoDB redo log buffer faster than background page cleaner threads can flush dirty pages to disk, triggering synchronous emergency flushing that freezes all active transactions.
We size innodb_redo_log_capacity to absorb peak write bursts and calibrate innodb_io_capacity_max to match physical NVMe storage controller bandwidth.
Query Plan Cache Invalidation & Parameter Sniffing Regressions
A prepared statement executed with atypical parameters generates an unindexed generic execution plan that gets cached by the engine, causing subsequent queries to perform catastrophic full table scans.
We rewrite volatile queries with explicit plan hints, configure plan cache modes, and create covering indexes that eliminate plan sensitivity to parameter selectivity.
Our engineers execute lightweight, read-only telemetry queries to isolate top slow queries without acquiring catalog locks or slowing transactions:
SELECT round(total_exec_time::numeric, 2) AS total_ms,
calls,
round(mean_exec_time::numeric, 2) AS avg_ms,
round((100 * total_exec_time / sum(total_exec_time) OVER ())::numeric, 2) AS pct_total,
query
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 5;SELECT query,
exec_count,
total_latency,
avg_latency,
rows_sent_avg,
rows_examined_avg,
full_scans
FROM sys.statement_analysis
WHERE rows_examined_avg > 1000
ORDER BY total_latency DESC
LIMIT 5;How JusDB Performance Tuning compares to alternative approaches.
Optimizing database performance requires deep knowledge of storage engines, execution plans, and memory concurrency. Here is how JusDB's Database SRE tuning compares to cloud advisors, APM tools, and in-house trial-and-error.
| Tuning Dimension | JusDB Performance SRE | Cloud Advisors (AWS/GCP) | APM Tools (Datadog) | In-House Guesswork |
|---|---|---|---|---|
| Root-Cause Query & Execution Plan Diagnostics | Deep execution plan forensics (EXPLAIN ANALYZE, buffer hits, sort spillovers, catalog locks) across the 24 database technologies we support | Surface-level CPU/IOPS graphs; zero insight into SQL execution plans or catalog internals | Captures latency spans and stack traces, but cannot diagnose internal storage engine bottlenecks | Ad-hoc query sampling; limited time or specialized tooling to isolate catalog-level regressions |
| Execution Safety & Lock Contention | Non-destructive, non-blocking telemetry chosen to avoid catalog or table locks during peak production | Read-only metrics, but recommendations lack workload context and often suggest disruptive instance restarts | Agent overhead can increase CPU and memory utilization during severe production latency spikes | High risk of triggering cascading lock storms while running blunt diagnostics under pressure |
| Turnkey Remediation Runbooks & Index DDL | Production-ready index DDL, rewritten queries, online schema migrations (gh-ost, pg_repack), and rollback safeguards | Generic automated suggestions ('Consider adding an index') without exact SQL syntax or rollback steps | Identifies slow endpoints without generating database-level index recommendations or parameter changes | Trial-and-error indexing causing redundant indexes, write amplification, and unneeded disk consumption |
| Kernel, Memory & Buffer Pool Alignment | Exact sysctl.conf, dirty_ratio, and engine buffer pool parameters calculated to hardware NVMe bandwidth | Locked default parameter groups based on instance memory ratios without workload-specific tuning | No visibility into Linux kernel memory management or storage controller flush queues | Standard OS defaults leading to swap thrashing, dirty page flush freezes, and OOM killer events |
| Security & Access Governance | Named, read-only accounts through the access path your team issues, under mutual NDA, with short-lived credentials and session logging where your tooling supports them | Broad Cloud IAM roles with customer tenant console access | Requires permanent agent installation with elevated host and database collector privileges | Unrestricted internal developer access with untracked ad-hoc diagnostic sessions |
| Measurable Latency & Verification | p95/p99 query latency benchmarked before and after each change, with continuous regression tracking | No performance SLA guarantees whatsoever ('Shared responsibility model') | Provides charts showing ongoing slowdowns without committing to latency improvement targets | Performance degrades over time as data volumes grow and sprint backlogs take priority |
Performance Optimization FAQ
Stop guessing. Start measuring.
JusDB audits your database performance from query to hardware — any engine, any cloud — and delivers ranked, quantified recommendations with implementation support.