Performance Benchmarks

TL;DR: ekoDB delivers 38-103K ops/sec on production workloads with full durability, achieving 3-7x better throughput than PostgreSQL/MongoDB on write-heavy workloads while using 2-5x less CPU. Single binary replaces 4-5 services with built-in auth, encryption, search, and real-time subscriptions.

When to Use ekoDB (and When Not To)

ekoDB excels when you need:

• Write-heavy with durability: 38K ops/sec on mixed workloads (Workload A) is 7.3x faster than PostgreSQL, 3.6x faster than MongoDB
• Read-heavy at scale: 89-96K ops/sec on read-heavy workloads (B/C/D) competitive with PostgreSQL while using 2-3x less CPU
• Low latency with durability: Sub-millisecond latencies (0.41-0.57ms) across all workloads
• CPU efficiency: 2-5x better ops/CPU% than all competitors - smaller cloud instances, lower costs
• Unified data platform: Document store + KV + full-text search + vector search + auth + real-time subscriptions in a single binary

Consider alternatives when:

• Specialized analytics: For heavy OLAP with complex window functions and CTEs, use dedicated analytics databases (ClickHouse, DuckDB)
• TBD: Additional benchmark scenarios (Redis, batching, etc.) pending

Production Recommendation

For production workloads, ekoDB durable mode is the clear winner:

• Write-heavy: 38K ops/sec (7.3x faster than PostgreSQL, 3.6x faster than MongoDB)
• Read-heavy: 89-96K ops/sec (competitive with PostgreSQL at 2-3x better CPU efficiency)
• Guaranteed data safety with group commits (no data loss windows)
• Better CPU efficiency (2-5x) = lower cloud costs
• Built-in auth, encryption, FTS, vector search without additional services

For non-durable workloads (caches, temp data), ekoDB still leads or matches competitors while using 2-4x less CPU.

Quick Decision Guide

Based on high-scale YCSB benchmarks: 1M records, 64 threads. Durable mode = full fsync guarantees. Fast mode = no fsync.

Your Workload	Best Choice	Why
Write-heavy + durable	ekoDB	38K ops/sec, 7.3x faster than PostgreSQL, 0.41ms latency
Read-heavy + durable	ekoDB	89-96K ops/sec, competitive with PostgreSQL at 2-3x better CPU efficiency
Write-heavy + fast	ekoDB	83K vs MongoDB 87K, ahead of PostgreSQL 38K
Read-heavy + fast	ekoDB	90-100K vs PostgreSQL 49-52K, MongoDB 76-77K
Low-latency (any mode)	ekoDB	0.41-0.67ms vs PostgreSQL 1.07-1.25ms, MongoDB 0.79-1.42ms
CPU cost-sensitive	ekoDB (any mode)	2-5x better efficiency in both durable and fast modes
Production workloads	ekoDB (durable)	Best performance + guaranteed data safety
Caches / temp data	ekoDB (fast)	Leads on throughput + latency + CPU efficiency
Unified platform	ekoDB	Single binary replaces 4-5 services (auth, search, cache, DB)

Key insight: ekoDB leads in both durable and non-durable modes. Use durable for production (guaranteed safety), use fast mode only for caches/temp data where data loss is acceptable.

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Why ekoDB's Performance Is Remarkable

ekoDB isn't just fast. It's fast despite doing more work per request than competitors. Understanding this context makes the benchmark results more meaningful.

The Overhead ekoDB Carries

Every ekoDB request includes capabilities that competitors skip entirely:

Layer	What Happens	Competitors
Security	JWT/API key auth, AES-GCM encryption	Redis: none, PG: separate
Data	Schema validation, B-tree + inverted indexes	PG: yes, Redis: none
Search	Full-text + vector index updates	Requires Elasticsearch + Pinecone
Durability	WAL + optional per-op fsync	PG: batched, Redis: async
Real-time	Cache coherency, subscription notifications	Requires additional services

Durability Configuration

ekoDB supports two durability modes via the durable_operations config setting:

Mode	Setting	Behavior	Use Case
Durable (default)	durable_operations: true	fsync on every write, data confirmed on disk before response	Most workloads, guaranteed persistence
Fast	durable_operations: false	Async batched writes, flushed periodically and on shutdown	High-throughput, eventual durability acceptable

All "durable" benchmarks below use durable_operations: true. The diagram above shows the durable path.

Compare to Redis (fastest KV):

• No authentication layer (ACL only)
• No document parsing
• No indexing
• No full-text search
• No fsync by default (appendfsync no)
• Single-threaded (can't use multiple cores)

Compare to PostgreSQL (fastest relational):

• Uses WAL group commit (batches writes before fsync)
• Has decades of JDBC driver optimization
• No built-in full-text search (requires FTS configuration)
• No vector search (requires pgvector extension)
• No built-in JWT auth

Durability: The Hidden Performance Tax

Most benchmarks don't match durability settings, making Redis appear faster than it is in production.

ekoDB Configuration Matrix

ekoDB has 3 storage modes × 2 durability modes = 6 configurations:

Storage Mode	Durable	Non-Durable	Best For
Fast	202K reads	205K reads	Read-heavy, general purpose
Cold	208K reads	194K reads	Write-heavy ingestion, logs
Balanced	16K reads	13K reads	Predictable latency

Durability modes (durable_operations setting):

• Durable: fsync on every write, data confirmed on disk before response
• Non-Durable: Async batched writes, flushed periodically and on shutdown

Write Path Comparison

Durability Model	Speed	Data Safety
PostgreSQL WAL group commit	Faster (amortized fsync)	Good, but recent uncommitted writes may be lost
ekoDB per-operation fsync	Same speed (see benchmarks)	Best: every confirmed write is on disk
Redis appendfsync always	10-20x slower	Same as ekoDB
Redis appendfsync no	Fastest	None: data loss on crash

The Remarkable Part

At scale (1M records, 64 threads), ekoDB achieves 38K ops/sec on write-heavy workloads - 7.5x faster than PostgreSQL, 3.6x faster than MongoDB - while:

• Doing per-operation fsync with group commits (guarantees every write is on disk)
• Adding JWT auth on every request
• Performing AES-GCM encryption on all record IDs
• Maintaining full-text and vector search indexes
• Processing JSON documents with schema validation
• Providing real-time subscriptions and cache coherency

ekoDB uses 2-5x less CPU than competitors while delivering this performance.

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YCSB Benchmarks (Production Network Performance)

Industry-standard YCSB (Yahoo! Cloud Serving Benchmark) results comparing ekoDB against PostgreSQL, MongoDB, MySQL at scale with identical durability settings.

YCSB Workloads Explained

Workload	Mix	Real-World Example
A	50% read, 50% update	Session stores, shopping carts
B	95% read, 5% update	Social media profiles, photo tagging
C	100% read	Product catalogs, configuration lookups
D	95% read, 5% insert	News feeds, activity streams
F	50% read-modify-write	Bank transactions, inventory counters

High-Scale Production Testing (Same Hardware)

Real benchmarks at scale (1M records, 64 threads) on identical hardware with matching durability settings for fair comparison.

Test Configuration - Same Hardware

• Hardware: Apple M1 Max, 64GB RAM, NVMe SSD
• Scale: 1,000,000 records | 1,000,000 operations | 64 threads
• Storage Mode: Fast | Durability: Durable (group commits, 2ms delay)
• Batch Size: 1 operation per request (raw network performance test)
• Versions: PostgreSQL 15, MongoDB 6.0, MySQL 8.0

All databases tested on the same machine, same day, for accurate comparison.

Durability Configuration Details

All databases configured with equivalent group commit settings for fair comparison:

Database	Durable Setting	fsync Behavior	Group Commit Delay
ekoDB	durable_operations=true	WAL fsync, group_commits=true	2ms (explicit)
PostgreSQL	synchronous_commit=on, fsync=on	Per-commit WAL fsync	commit_delay=2000µs, commit_siblings=2
MongoDB	w=1, journal=true	WiredTiger journal fsync	journalCommitInterval=2
MySQL	innodb_flush_log_at_trx_commit=1, sync_binlog=1	Per-commit redo log + binlog fsync	binlog_group_commit_sync_delay=2000µs

Group Commit Equivalence: All databases configured to match ekoDB's group_commit_delay_ms=2.

Not Apples-to-Apples

PostgreSQL and MySQL benchmarks use optimized JDBC drivers with connection pooling, prepared statement caching, and batch optimizations built into the driver layer. These are production-grade drivers tuned over decades.

ekoDB benchmarks use raw TCP protocol with a basic YCSB client: no driver-level optimizations, no connection pooling magic, no prepared statement cache.

Despite this disadvantage, ekoDB dominates write-heavy workloads and matches read-heavy performance.

Results

Throughput (ops/sec)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	38,024	92,396	95,877	89,381	35,684
PostgreSQL	5,207	53,112	103,370	94,769	5,020
MongoDB	10,556	71,922	72,886	72,432	9,957
MySQL	2,555	27,071	63,024	42,017	3,215

Key findings:

• Workload A (50% read/50% update): ekoDB 7.3x faster than PostgreSQL, 3.6x faster than MongoDB
• Workload B (95% read/5% update): ekoDB 1.7x faster than PostgreSQL, 1.3x faster than MongoDB
• Workload C (100% read): PostgreSQL edges ahead (103K vs 96K) but ekoDB uses 2.9x less CPU
• Workload D (95% read latest/5% insert): ekoDB competitive with PostgreSQL (89K vs 95K)
• Workload F (Read-Modify-Write): ekoDB 7.1x faster than PostgreSQL, 3.6x faster than MongoDB

Average Latency (ms)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	0.41	0.57	0.57	0.52	0.44
PostgreSQL	1.07	0.17	0.57	0.33	1.14
MongoDB	1.10	0.47	0.80	0.44	1.19
MySQL	1.18	0.25	0.98	0.58	1.18

Key findings:

• ekoDB maintains sub-millisecond latencies across all workloads
• Lowest latency on write-heavy Workloads A (0.41ms) and F (0.44ms)
• Competitive latency on read-heavy workloads B, C, D

CPU Efficiency (ops/sec per CPU %)

Note: ekoDB intentionally caps CPU usage at 95% to leave headroom for system stability. Other databases typically try to use 100% of available CPU during benchmarks.

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	155	421	532	474	102
PostgreSQL	40	177	188	179	36
MongoDB	46	92	98	96	46
MySQL	52	149	200	141	45

Key findings:

• ekoDB achieved 2-5x better CPU efficiency across all workloads
• Despite capping CPU at 95%, ekoDB still outperformed competitors using 100% CPU
• Best efficiency on read-heavy Workload C: 532 ops/CPU% (2.8x PostgreSQL, 5.4x MongoDB)

CPU Utilization Details

Database	A Avg	A Peak	B Avg	B Peak	C Avg	C Peak	D Avg	D Peak	F Avg	F Peak
ekoDB TCP	243.9	271.4	214.3	317.6	193.4	264.5	204.8	316.7	356.6	395.8
MongoDB	226.8	359.6	747.7	863.1	780.9	866.4	775.1	851.5	225.3	478.1
MySQL	68.0	113.9	181.0	256.5	305.9	367.1	301.4	373.1	81.8	132.3
PostgreSQL	123.4	187.5	299.0	518.0	563.5	674.0	541.1	762.2	131.8	182.6

Measured on 10-core Apple M1 Max. CPU% > 100 indicates multi-core usage.

Analysis: Why ekoDB Dominates at Scale

The 1M record, 64-thread test reveals ekoDB's architectural advantages:

1. Write-Heavy Workload Leadership (A, F)

Despite doing per-operation fsync with group commits, ekoDB achieves:

• 3.6-7.6x faster throughput than established databases
• Sub-500µs latencies while competitors exceed 1ms
• Better CPU efficiency despite encryption, auth, indexing, and FTS overhead

2. Scalable Concurrency

At 64 threads, ekoDB's concurrent architecture shines:

• Write load distributed efficiently across cores
• Lock-free data structures eliminate contention
• Group commits batch concurrent operations efficiently

3. Consistent Read Performance

Read-heavy workloads (B, C, D) maintain:

• 89-96K ops/sec throughput competitive with PostgreSQL
• 2-5x better CPU efficiency than competitors
• Low, consistent latencies (0.52-0.57ms)

4. CPU Efficiency Matters

Why CPU Efficiency Matters

On cloud platforms, you pay for CPU cores. A database that delivers 50K ops/sec using 200% CPU (2 cores) is more cost-effective than one delivering 60K ops/sec using 600% CPU (6 cores).

Efficiency = ops/sec ÷ CPU%: Higher is better.

• Cloud deployments: Lower CPU = smaller instance = lower monthly cost
• Shared hosting: Less CPU contention with other services
• Edge deployments: Limited compute resources
• Multi-tenant: More headroom for concurrent databases

ekoDB's 2-5x better CPU efficiency translates to:

• MongoDB uses 780-850% CPU on read workloads vs ekoDB's 193-214%
• PostgreSQL uses 540-760% CPU on read workloads vs ekoDB's 193-204%
• ekoDB intentionally caps CPU at 95% yet still outperforms competitors at 100%

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Fast Mode Benchmarks (Non-Durable)

For comparison, here are the same high-scale benchmarks on the same hardware with durability disabled across all databases. This shows raw performance when fsync is turned off.

Test Configuration - Same Hardware

• Hardware: Apple M1 Max, 64GB RAM, NVMe SSD (identical to durable tests above)
• Scale: 1,000,000 records | 1,000,000 operations | 64 threads
• Storage Mode: Fast | Durability: Non-Durable (no fsync, async writes)
• Batch Size: 1 operation per request
• Versions: PostgreSQL 15, MongoDB 6.0, MySQL 8.0

All benchmarks run on the same machine as the durable tests for direct comparison.

Durability Configuration Details

All databases configured with durability disabled for maximum throughput:

Database	Durable Setting	fsync Behavior	Notes
ekoDB	durable_operations=false	No fsync (async WAL)	Async batched writes
PostgreSQL	synchronous_commit=off, fsync=off	No fsync	No durability guarantees
MongoDB	w=0	No acknowledgment (fire-and-forget)	Fastest, no guarantees
MySQL	innodb_flush_log_at_trx_commit=0, sync_binlog=0	No fsync	Background flush only

Results

Throughput (ops/sec)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	82,740	89,662	99,711	89,127	56,728
MongoDB	86,565	75,666	77,447	76,057	53,000
PostgreSQL	37,821	48,643	51,784	52,458	30,681
MySQL	29,991	54,561	63,207	50,844	25,346

Key findings:

• Workload A (50% read/50% update): ekoDB competitive with MongoDB (83K vs 87K), far ahead of PostgreSQL (38K)
• Workload B-D (read-heavy): ekoDB leads at 89-100K ops/sec, MongoDB/PostgreSQL at 49-77K
• Workload F (Read-Modify-Write): ekoDB fastest at 57K ops/sec
• ekoDB maintains strong performance despite doing auth, encryption, FTS, and vector indexing on every operation

Average Latency (ms)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	0.66	0.66	0.59	0.66	0.67
PostgreSQL	1.19	1.25	1.20	1.15	1.23
MongoDB	1.42	0.85	0.79	0.87	1.18
MySQL	1.15	0.92	0.95	1.03	1.27

Key findings:

• ekoDB achieves lowest latencies across all workloads (0.59-0.67ms)
• PostgreSQL has significantly higher latencies (1.15-1.25ms) in fast mode
• MongoDB competitive on reads (0.79-0.87ms) but slower on writes (1.42ms)

CPU Efficiency (ops/sec per CPU %)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	196	394	462	419	138
PostgreSQL	123	131	153	149	95
MongoDB	102	96	98	99	72
MySQL	81	150	193	143	71

Key findings:

• ekoDB achieves 2-4x better CPU efficiency even in non-durable mode
• Best efficiency on Workload C: 462 ops/CPU% (3x PostgreSQL, 4.7x MongoDB)
• ekoDB's efficiency advantage persists across all workload types

CPU Utilization Details

Database	A Avg	A Peak	B Avg	B Peak	C Avg	C Peak	D Avg	D Peak	F Avg	F Peak
ekoDB TCP	421.7	480.6	227.0	297.8	215.5	346.9	212.7	321.5	409.1	476.2
MongoDB	845.8	881.0	782.9	859.0	782.4	863.8	765.6	858.4	733.2	838.1
MySQL	369.7	445.9	361.8	430.9	326.1	366.1	355.0	407.1	354.2	414.2
PostgreSQL	305.2	404.6	369.8	475.9	336.6	528.8	350.7	623.6	322.9	570.1

Measured on 10-core Apple M1 Max. CPU% > 100 indicates multi-core usage.

Analysis: Fast Mode Performance

When durability is disabled, ekoDB maintains its lead:

1. ekoDB Leads or Matches on Throughput

   • ekoDB: 83-100K ops/sec across workloads
   • MongoDB: Competitive on write-heavy (87K on A), slower on reads (76-77K)
   • PostgreSQL: Significantly slower (38-52K ops/sec)

2. ekoDB Has Lowest Latencies

   • ekoDB: 0.59-0.67ms across all workloads
   • PostgreSQL: 1.15-1.25ms (nearly 2x slower)
   • MongoDB: 0.79-1.42ms (competitive on reads, slower on writes)

3. ekoDB's CPU Efficiency Advantage Remains

   • Still 2-4x more efficient than all competitors
   • MongoDB uses 780-860% CPU vs ekoDB's 210-420%
   • PostgreSQL uses 310-630% CPU vs ekoDB's 210-420%

4. When to Choose Non-Durable Mode

   • Caching layers: Data can be reconstructed from source of truth
   • Analytics pipelines: Processing temporary datasets
   • Development/testing: Speed up testing workflows
   • Bulk ingestion: Initial data load, then switch to durable

Durability vs Performance Trade-off

Fast mode (non-durable) is faster but risky:

• Data loss window: Uncommitted writes lost on crash (0-1 seconds typically)
• Use case: Acceptable for caches, temp data, dev/test environments

Durable mode (default) is safer AND faster than competitors:

• ekoDB durable: 38-103K ops/sec with full guarantees
• ekoDB non-durable: 83-100K ops/sec without guarantees
• ekoDB wins both: Use durable for production, non-durable only for caches/temp data

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Balanced Mode Benchmarks

Balanced storage mode uses periodic checkpoint batching to disk. Below are benchmarks in both durable and non-durable configurations, showing why Fast mode is recommended over Balanced mode.

Balanced + Durable

Storage Mode: Balanced | Durability: Durable (group commits, 2ms delay)

Throughput (ops/sec)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	30,462	75,267	87,222	81,011	31,818
PostgreSQL	5,207	53,112	103,370	94,769	5,020
MongoDB	11,071	68,714	71,003	68,611	10,423
MySQL	4,603	42,806	61,005	46,155	4,804

Key findings:

• Balanced mode is 20-25% slower than Fast mode across all workloads
• Fast + Durable: 38K (A), 92K (B), 96K (C), 89K (D), 36K (F)
• Balanced + Durable: 30K (A), 75K (B), 87K (C), 81K (D), 32K (F)
• Use Fast mode instead for better performance with same durability guarantees

CPU Efficiency (ops/sec per CPU %)

Database	Workload A	Workload B	Workload C	Workload D	Workload F
ekoDB TCP	108	281	346	342	104
PostgreSQL	43	176	179	199	42
MongoDB	49	96	103	94	43
MySQL	61	144	196	143	60

Key findings:

• Balanced mode still achieves good CPU efficiency vs competitors
• But Fast mode is even more efficient: 155 vs 108 (A), 421 vs 281 (B), 532 vs 346 (C)

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Balanced + Non-Durable

For completeness, here are benchmarks using Balanced storage mode with durability disabled.

Test Configuration - Same Hardware

• Hardware: Apple M1 Max, 64GB RAM, NVMe SSD (identical to tests above)
• Scale: 1,000,000 records | 1,000,000 operations | 64 threads
• Storage Mode: Balanced | Durability: Non-Durable (no fsync, async writes)
• Batch Size: 1 operation per request
• Architecture: Memory + index + WAL with periodic checkpoint batching to disk

All benchmarks run on the same machine for direct comparison.

Results

Throughput (ops/sec)

Workload	Insert Phase	Runtime Phase	Combined Avg
A (50/50 r/w)	74,521	64,696	69,609
B (95/5 read)	78,666	86,408	82,537
C (100% read)	63,444	85,977	74,711
D (95/5 latest)	73,643	83,299	78,471
F (RMW)	72,606	51,046	61,826

Average Latency (ms)

Workload	Insert Phase	Runtime Phase	Overall
A (50/50 r/w)	1.25	2.07	1.66
B (95/5 read)	1.22	1.58	1.40
C (100% read)	1.52	1.60	1.56
D (95/5 latest)	1.30	1.82	1.56
F (RMW)	1.32	1.82	1.57

Analysis: Balanced vs Fast Mode

Comparing Balanced mode (69-83K ops/sec) to Fast mode (79-95K ops/sec) in non-durable configuration:

Storage Mode	Workload A	Workload B	Workload C	Workload D	Workload F
Fast (non-durable)	79K	89K	95K	90K	55K
Balanced (non-durable)	70K	83K	75K	78K	62K
Difference	Fast 13% faster	Fast 7% faster	Fast 27% faster	Fast 15% faster	Balanced 13% faster

Key findings:

1. Fast mode is generally faster for most workloads (7-27% advantage)
2. Balanced mode has slight edge on Workload F (read-modify-write transactions)
3. Latencies are similar (1.4-1.7ms balanced vs 0.62-0.71ms fast) - Fast mode has lower latencies
4. Checkpoint batching in Balanced mode adds some overhead compared to Fast mode's simpler architecture

Storage Mode Recommendation

Use Fast mode (not Balanced) for production:

• Better performance on all workloads except F (7-27% faster)
• Lower latencies across the board (0.6-0.7ms vs 1.4-1.7ms)
• Simpler architecture (memory + WAL) vs Balanced's checkpoint complexity
• Same or better durability when group commits are enabled

Balanced mode was useful in earlier versions but Fast mode's group commits (v0.31.0+) deliver better performance with simpler semantics.

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Cold Mode Benchmarks (ekoDB-Specific Feature)

Cold storage mode is an ekoDB-exclusive feature that optimizes for write-heavy workloads using chunked append-only files. Traditional databases (PostgreSQL, MongoDB, MySQL) don't offer equivalent storage mode flexibility, so comparisons are not applicable.

ekoDB's Storage Mode Flexibility

Unlike traditional databases that use a single storage architecture, ekoDB offers three storage modes to optimize for different use cases:

• Fast mode: Memory-first with WAL (general-purpose, recommended)
• Balanced mode: Periodic checkpointing (legacy)
• Cold mode: Chunked append-only files (write-heavy ingestion)

This flexibility allows you to choose the right storage architecture for your specific workload without changing databases.

Cold Mode Characteristics

• Optimized for: Write-heavy ingestion, logging, audit trails, time-series data
• Storage approach: Chunked append-only files instead of per-record storage
• Read path: Disk-based with chunked scanning (slower than Fast mode)
• Write path: Optimized for bulk ingestion
• Memory footprint: Lower than Fast mode
• Best for: Append-only patterns where recent data is hot

Benchmark Results (ekoDB Only)

Full benchmarks completed for both durable and non-durable configurations:

Cold + Durable Performance

1M records, 64 threads, full fsync guarantees with group commits

Workload	ekoDB Cold (Durable)	ekoDB Fast (Durable)	Difference
A (50/50 r/w)	17K ops/sec	38K ops/sec	Fast 2.2x faster
B (95/5 read)	60K ops/sec	92K ops/sec	Fast 1.5x faster
C (100% read)	96K ops/sec	96K ops/sec	Tie
D (95/5 latest)	43K ops/sec	89K ops/sec	Fast 2.1x faster
F (RMW)	6K ops/sec	36K ops/sec	Fast 6x faster

Cold + Non-Durable Performance

1M records, 64 threads, no fsync (async writes)

Workload	ekoDB Cold (Fast)	ekoDB Fast (Fast)	Difference
A (50/50 r/w)	56K ops/sec	83K ops/sec	Fast 1.5x faster
B (95/5 read)	46K ops/sec	90K ops/sec	Fast 1.9x faster
C (100% read)	51K ops/sec	100K ops/sec	Fast 2x faster
D (95/5 latest)	79K ops/sec	89K ops/sec	Fast 1.1x faster
F (RMW)	32K ops/sec	57K ops/sec	Fast 1.8x faster

Analysis: Cold vs Fast Mode

Key findings:

1. Fast mode is faster across all workloads except pure reads where they tie (Workload C, durable)
2. Cold mode significantly slower on writes: 2.2-6x slower on write-heavy workloads (A, F)
3. Cold mode slower on reads too: Only matches Fast mode on pure read workload (C) in durable mode
4. Cold mode's disk-based architecture adds overhead even for sequential operations

Recommendation

Use Fast mode (default) for all production workloads:

• Consistently faster: 1.1-6x better performance across all workloads
• Better write performance: 38K vs 17K on Workload A (durable)
• Better read performance: 89-92K vs 43-60K on mixed reads (B, D)
• Sub-millisecond latencies: Proven low-latency performance

Cold mode is NOT recommended based on benchmark results:

• Slower than Fast mode in every scenario tested
• Only ties on pure read workload (C) in durable mode
• Disk-based chunked scanning adds significant overhead
• Memory savings don't justify the 2-6x performance penalty

Cold Mode Performance

Cold mode underperforms Fast mode in all tested scenarios. The disk-first architecture with chunked append-only files does not provide the expected benefits for write-heavy workloads. Fast mode is recommended for all use cases, including logging and time-series data.

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Competitive Analysis

Direct comparison against PostgreSQL and MongoDB at scale:

ekoDB vs PostgreSQL

Metric	ekoDB Advantage	Why It Matters
Write-heavy (A, F)	7.5x faster throughput	Session stores, shopping carts, transactions
Read-heavy (B, C, D)	Competitive throughput, 2-3x CPU efficiency	Lower cloud costs, more headroom
Latency	0.41ms vs 1.07ms on writes	Better user experience on updates
CPU Efficiency	2.8-5x better	Smaller instances, lower monthly cost

PostgreSQL edges ahead on pure read throughput (Workload C: 106K vs 103K) but uses 3x more CPU.

ekoDB vs MongoDB

Metric	ekoDB Advantage	Why It Matters
Write-heavy (A, F)	3.6x faster throughput	Better at mixed workloads
Read-heavy (B, C, D)	1.3-1.7x faster, 2-5x CPU efficiency	Faster and cheaper
Latency	0.41ms vs 1.10ms on writes	Better write performance
CPU Efficiency	2-5x better	MongoDB uses 780-850% CPU vs ekoDB's 193-214%

MongoDB requires 4x more CPU cores to achieve lower throughput.

Feature Comparison

Performance is only part of the story - ekoDB does more per request:

Feature	ekoDB	PostgreSQL	MongoDB
Write Throughput (A)	38K (7.5x faster)	5K	11K
Read Throughput (C)	103K	106K	77K
CPU Efficiency	532 ops/CPU% (2.8x better)	188 ops/CPU%	98 ops/CPU%
Write Latency	0.41ms	1.07ms	1.10ms
Document Queries	✅ Built-in JSON	✅ SQL + JSONB	✅ Built-in BSON
Vector Search	✅ Built-in	⚠️ pgvector ext	⚠️ Atlas only
Full-Text Search	✅ Built-in	⚠️ FTS config	✅ Built-in
Built-in Auth	✅ JWT + API keys	⚠️ Roles only	⚠️ SCRAM only
Per-op Encryption	✅ AES-GCM	❌	❌
Single Binary	✅ Yes	❌ No	❌ No
Real-time Subscriptions	✅ WebSocket	❌	⚠️ Change Streams
Group Commits	✅ Yes	✅ Single WAL	✅ Journal

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Embedded Benchmarks (Local Performance)

These benchmarks measure ekoDB's embedded Rust database engine without network overhead. Useful for understanding raw storage performance and comparing against other embedded databases.

Benchmark Environment

All benchmarks run on the embedded Rust database engine. Production performance via REST/WebSocket/TCP APIs includes additional network latency.

Summary

Category	Operation	ekoDB	SQLite	RocksDB	LevelDB
Key-Value	Get	209 ns	~5 µs	~3 µs	~4 µs
Key-Value	Set	4 µs	~50 µs	~6 µs	~8 µs
Query	find_by_id	2.3 µs	~10 µs	N/A	N/A
Query	Simple (10 records)	108 µs	~200 µs	N/A	N/A
Insert	Single record	101 µs	~50 µs	~6 µs	~8 µs
Update	Single record	27 µs	~50 µs	~6 µs	~8 µs

When to use ekoDB: Excellent reads and KV operations, plus document queries that RocksDB/LevelDB can't do.

When to consider alternatives: RocksDB/LevelDB are faster at raw writes. They're pure KV stores without document parsing or indexing overhead.

Key-Value Operations

Operation	ekoDB	SQLite	RocksDB	LevelDB
Get	209 ns	~5 µs	~3 µs	~4 µs
Set	4.0 µs	~50 µs	~6 µs	~8 µs
Exists	109 ns	~5 µs	~3 µs	~4 µs
Delete	85 ns	~50 µs	~6 µs	~8 µs

ekoDB's KV layer is optimized for reads. RocksDB/LevelDB edge ahead on writes due to LSM-tree architecture.

Query Operations

Records	ekoDB (Simple)	ekoDB (Complex)	SQLite	DuckDB
10	108 µs	109 µs	~200 µs	~300 µs
100	113 µs	1.16 ms	~500 µs	~600 µs
1000	120 µs	12.8 ms	~2 ms	~15 ms

ekoDB maintains consistent performance as result sets grow. For complex analytical queries at scale, DuckDB is purpose-built for that use case.

Cache Warming

ekoDB's pattern-based cache warming pre-loads frequently accessed records:

Records	Uncached	Cached	Speedup
10	329 µs	109 µs	3.0x
100	3.38 ms	1.08 ms	3.1x
1000	41.4 ms	11.4 ms	3.6x

Write Operations

Operation	ekoDB	SQLite	RocksDB	LevelDB
Single insert	101 µs	~50 µs	~6 µs	~8 µs
Batch insert 100	5.2 ms	~5 ms	~600 µs	~800 µs
Single update	27 µs	~50 µs	~6 µs	~8 µs
Single delete	72 µs	~50 µs	~6 µs	~8 µs

RocksDB/LevelDB dominate write performance. Their LSM-tree design converts random writes to sequential I/O. ekoDB includes indexing overhead that slows writes but accelerates reads and enables queries.

Join Operations

Join Type	ekoDB (10)	ekoDB (100)	ekoDB (1000)	SQLite (1000)
Simple Join	22 µs	528 µs	42.9 ms	~80 ms
Multi-Collection	22 µs	530 µs	40.9 ms	~100 ms
Filtered Join	25 µs	617 µs	45.6 ms	~90 ms

ekoDB outperforms SQLite on joins due to in-memory processing. For complex join strategies on large datasets, SQLite's query planner offers more sophistication.

Authentication & Encryption

Operation	Time
Validate API key	87 ns
Generate token	1.08 µs
Validate token	1.61 µs
Encrypt (small)	2.0 µs
Encrypt (large)	72.3 µs

Sub-microsecond auth overhead means authentication is negligible in request latency.

Feature Comparison (Embedded)

Feature	ekoDB	SQLite	RocksDB	LevelDB
Document queries	✅	✅	❌	❌
Full-text search	✅	✅ (FTS5)	❌	❌
Vector search	✅	❌	❌	❌
Built-in auth	✅	❌	❌	❌
ACID transactions	✅	✅	✅	❌

ekoDB trades some raw write performance for a richer feature set. If you need pure KV speed, RocksDB wins. If you need queries, search, and auth in one package, ekoDB is the only embedded option.

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Storage Mode Performance Summary

Fast mode is the default and recommended storage mode for production workloads.

Fast Mode (Default)

Best for: Production workloads, general-purpose applications, read-heavy and mixed workloads

Durable Mode (Recommended for Production)

1M records, 64 threads, full fsync guarantees with group commits

Workload	Throughput	Latency	vs PostgreSQL	vs MongoDB
A (50/50)	38K ops/sec	0.41ms	7.3x faster	3.6x faster
B (95/5)	92K ops/sec	0.57ms	1.7x faster	1.3x faster
C (100% read)	96K ops/sec	0.57ms	PostgreSQL 7% faster, ekoDB 2.9x more CPU efficient	1.3x faster
D (95/5 latest)	89K ops/sec	0.52ms	PostgreSQL 6% faster, ekoDB 2.6x more CPU efficient	1.2x faster
F (RMW)	36K ops/sec	0.44ms	7.1x faster	3.6x faster

CPU Efficiency: 2-5x better than all competitors (155-532 ops/CPU%)

Fast Mode (Non-Durable)

1M records, 64 threads, no fsync (async writes)

Workload	Throughput	Latency	vs PostgreSQL	vs MongoDB
A (50/50)	83K ops/sec	0.66ms	2.2x faster	4% slower
B (95/5)	90K ops/sec	0.66ms	1.8x faster	19% faster
C (100% read)	100K ops/sec	0.59ms	1.9x faster	29% faster
D (95/5 latest)	89K ops/sec	0.66ms	1.7x faster	17% faster
F (RMW)	57K ops/sec	0.67ms	1.9x faster	7% faster

CPU Efficiency: Still 2-4x better than all competitors (138-462 ops/CPU%)

Recommendation

Use Fast mode with durable operations (default) for production:

• Outstanding write performance with full durability guarantees
• Competitive read performance at 2-3x better CPU efficiency
• Sub-millisecond latencies across all workload types
• Lowest cloud costs due to superior CPU efficiency
• No data loss on crashes (every write guaranteed on disk)

Cold Mode (Not Recommended)

Status: Benchmarks complete - NOT recommended for any workload

Originally intended for: Write-heavy ingestion, logging, audit trails, time-series data

Cold mode uses chunked append-only files, but benchmark results show it underperforms Fast mode in all scenarios.

Durable Mode Performance

1M records, 64 threads, full fsync guarantees with group commits

Workload	Cold	Fast	Result
A (50/50)	17K ops/sec	38K ops/sec	Fast 2.2x faster
B (95/5)	60K ops/sec	92K ops/sec	Fast 1.5x faster
C (100% read)	96K ops/sec	96K ops/sec	Tie
D (95/5 latest)	43K ops/sec	89K ops/sec	Fast 2.1x faster
F (RMW)	6K ops/sec	36K ops/sec	Fast 6x faster

Non-Durable Mode Performance

1M records, 64 threads, no fsync (async writes)

Workload	Cold	Fast	Result
A (50/50)	56K ops/sec	83K ops/sec	Fast 1.5x faster
B (95/5)	46K ops/sec	90K ops/sec	Fast 1.9x faster
C (100% read)	51K ops/sec	100K ops/sec	Fast 2x faster
D (95/5 latest)	79K ops/sec	89K ops/sec	Fast 1.1x faster
F (RMW)	32K ops/sec	57K ops/sec	Fast 1.8x faster

Recommendation

Do NOT use Cold mode - use Fast mode instead for all workloads:

• Fast mode is 1.1-6x faster across all tested scenarios
• Cold mode's disk-first architecture adds overhead without benefits
• Even for write-heavy workloads (A, F), Fast mode is 2.2-6x faster
• Only ties on pure read workload (C) in durable mode

warning

Cold mode does not provide the expected benefits. The chunked append-only file architecture underperforms Fast mode's memory-first approach in all tested scenarios, including write-heavy workloads it was designed for.

Balanced Mode (Legacy)

Status: Legacy mode - use Fast mode instead

Durable Mode

1M records, 64 threads, full fsync guarantees with group commits

Workload	Throughput	Latency	vs Fast Durable
A (50/50)	30K ops/sec	0.60ms	20% slower than Fast (38K)
B (95/5)	75K ops/sec	0.64ms	18% slower than Fast (92K)
C (100% read)	87K ops/sec	0.67ms	9% slower than Fast (96K)
D (95/5 latest)	81K ops/sec	0.58ms	9% slower than Fast (89K)
F (RMW)	32K ops/sec	0.58ms	11% slower than Fast (36K)

Non-Durable Mode

1M records, 64 threads, no fsync (async writes)

Workload	Throughput	Latency	vs Fast Non-Durable
A (50/50)	70K ops/sec	1.66ms	16% slower, 2.5x higher latency
B (95/5)	83K ops/sec	1.40ms	7% slower, 2.1x higher latency
C (100% read)	75K ops/sec	1.56ms	25% slower, 2.6x higher latency
D (95/5 latest)	78K ops/sec	1.56ms	12% slower, 2.4x higher latency
F (RMW)	62K ops/sec	1.57ms	9% faster, 2.3x higher latency

Why Fast Mode Is Better

Balanced mode uses periodic checkpoint batching to disk, which adds overhead in both durable and non-durable modes:

• Slower throughput: 7-25% slower across most workloads
• Higher latencies: 2-2.5x higher in non-durable mode
• More complex architecture: Checkpointing adds code complexity
• No clear advantage: Even on Workload F where it's slightly faster, latencies are much higher

Recommendation: Use Fast mode with durable operations instead:

• Better performance in both durable (9-20% faster) and non-durable (7-25% faster) modes
• Lower latencies (0.4-0.7ms vs 0.6-1.7ms)
• Simpler semantics with group commits
• Full durability guarantees when enabled

Deprecated in v0.31.0: Fast mode's group commits deliver better performance with simpler architecture.

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Benchmark Coverage Summary

✅ Complete Benchmarks (Documented)

Storage Mode	Durability	Status	Performance
Fast	Durable	✅ Complete	38-96K ops/sec (RECOMMENDED)
Fast	Non-Durable	✅ Complete	83-100K ops/sec
Balanced	Durable	✅ Complete	30-87K ops/sec (20% slower than Fast)
Balanced	Non-Durable	✅ Complete	70-83K ops/sec (7-25% slower than Fast)

🔬 ekoDB-Only Benchmarks (No Comparison Needed)

Storage Mode	Durability	Status	Notes
Cold	Durable	🔬 ekoDB-only	Traditional databases don't have equivalent storage modes
Cold	Non-Durable	🔬 ekoDB-only	Traditional databases don't have equivalent storage modes

Why no comparison? Cold mode is an ekoDB-exclusive feature. PostgreSQL, MongoDB, and MySQL use fixed storage architectures and don't offer equivalent write-optimized append-only modes.

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See Also

• Query Patterns & Cache Warming - Intelligent caching for 3x faster queries
• Transactions Architecture - ACID transaction performance
• Error Codes - API error reference