Search Engine Load Test Report: ParadeDB vs Elasticsearch

Search Engine Load Test Report

ParadeDB (PostgreSQL BM25) vs Elasticsearch

Proof of Concept — Performance Benchmark

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Prepared by: Norman Fwamba Date: 22 April 2026 Test Tool: k6 v1.7.1 Application: SearchPOC (.NET 9, EF Core, Docker) Environment: Local (Windows 11, Docker Desktop)

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Table of Contents

1. Executive Summary
2. What Are We Comparing?
3. How the System Works
4. Test Setup & Methodology
5. ParadeDB — Detailed Results
6. Elasticsearch — Detailed Results
7. Head-to-Head Comparison
8. What the Numbers Mean (Plain English)
9. Infrastructure & Operational Comparison
10. Cost Analysis
11. Risk Assessment
12. Recommendation
13. Appendix — Technical Details

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1. Executive Summary

We conducted a rigorous load test comparing two search technologies:

• ParadeDB — a modern search extension built on top of PostgreSQL (the world’s most popular open-source database)
• Elasticsearch — the industry-standard dedicated search engine, widely used by Netflix, LinkedIn, and GitHub

Both were tested under identical conditions: up to 100 simultaneous users, over a 3-minute test window, performing real-world operations (creating records and searching them).

Key Findings at a Glance

What We Measured	ParadeDB	Elasticsearch	Winner
Average search speed	194 ms	33 ms	Elasticsearch
Speed at 95th percentile	651 ms	101 ms	Elasticsearch
Requests per second	44.9	55.7	Elasticsearch
Error rate	0%	0%	Tie
Data write (create) avg	264 ms	47 ms	Elasticsearch
Passed all thresholds	No	Yes	Elasticsearch

Bottom Line: Elasticsearch is significantly faster than ParadeDB under load — roughly 5.8x faster on average search queries and 6.4x faster at peak (p95). Both had zero errors, meaning both are reliable. The difference is purely in speed and capacity.

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2. What Are We Comparing?

2.1 ParadeDB

Think of it like this: ParadeDB is like upgrading your existing filing cabinet with a supercharged search tab. Instead of replacing your whole storage system, you just make the search capability much smarter.

Technically:

• ParadeDB is PostgreSQL (a traditional database) with a special search index called BM25 added on top
• BM25 (Best Match 25) is a well-known algorithm for ranking text search results by relevance
• All your data lives in one place — the same database that stores everything else
• No extra server, no extra service, no extra cost

2.2 Elasticsearch

Think of it like this: Elasticsearch is like hiring a dedicated librarian whose only job is to index and find books. Incredibly fast at searching, but it’s a separate person you need to manage, pay, and keep in sync with the main library.

Technically:

• Elasticsearch is a standalone search engine built specifically for fast full-text search
• It stores a copy of your data in its own optimised format
• Runs as a separate service alongside your main database
• Used by some of the largest companies in the world for high-volume search

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3. How the System Works

The POC application (SearchPOC) was built to test both technologies fairly under the same conditions.

User Request
     |
     v
.NET 9 Web API (SearchPOC)
     |
     +------------------+
     |                  |
     v                  v
 ParadeDB           Elasticsearch
 (Port 5435)        (Port 9200)
 PostgreSQL +        Dedicated
 BM25 Index          Search Index

Data flow:

1. When a bank record is created, it is saved to PostgreSQL (ParadeDB) AND simultaneously sent to Elasticsearch
2. When a search is performed, the request goes to either ParadeDB or Elasticsearch depending on the mode selected
3. Both return a list of matching bank records ranked by relevance

Data model tested:

Field	Type	Description
ID	UUID	Unique identifier
Name	Text	Bank name (e.g., “Standard Bank-482931”)
Active	Boolean	Whether bank is currently active
DateCreated	DateTime	When the record was added

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4. Test Setup & Methodology

4.1 Test Environment

Component	Details
Machine	Windows 11, Local Developer Machine
Application	.NET 9 Minimal API, dotnet run
ParadeDB	Docker container, port 5435
Elasticsearch	Docker v8.13.0, port 9200
Load Test Tool	k6 v1.7.1 (Grafana)
Test Duration	3 minutes per engine
Max Virtual Users	100

4.2 Load Profile (Ramp-Up Pattern)

Both tests used identical stages:

Stage	Duration	Users	Purpose
1 — Warm-up	30 seconds	10	Let the system settle
2 — Ramp-up	60 seconds	50	Gradual increase
3 — Peak Load	60 seconds	100	Maximum stress
4 — Cool-down	30 seconds	0	Graceful shutdown

4.3 What Each Virtual User Did

Every simulated user repeated this loop continuously:

1. CREATE a new bank record with a random name
2. Wait 0.5 seconds
3. SEARCH for banks using a keyword (e.g., “Standard”, “FNB”, “Discovery”)
4. Wait 1 second
5. Repeat

4.4 Performance Thresholds (Pass/Fail Criteria)

Metric	Acceptable Level	Meaning
p(95) search latency	< 600ms	95% of searches must complete in under 0.6 seconds
p(99) search latency	< 1000ms	99% of searches must complete in under 1 second
Success rate	> 95%	At least 95 out of every 100 requests must succeed
Error rate	< 5%	Fewer than 1 in 20 requests can fail

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5. ParadeDB — Detailed Results

5.1 Summary

ParadeDB completed 4,058 search iterations with zero errors but breached both latency thresholds under peak load.

5.2 Core Metrics

Metric	Value
Total Iterations	4,058
Total HTTP Requests	8,116
Throughput	44.94 requests/second
Error Rate	0.00%
Success Rate	100.00%
Data Received	22 MB (122 kB/s)
Data Sent	1.1 MB (6.2 kB/s)

5.3 Search Latency Breakdown

Percentile	Time	Notes
Minimum	15.61 ms	Fastest single search
Average	194.85 ms	Typical search time
Median (p50)	117.49 ms	Half of searches faster than this
p(90)	441.42 ms	90% of searches faster than this
p(95)	651.65 ms	FAILED — threshold was 600ms
p(99)	1,130 ms	FAILED — threshold was 1,000ms
Maximum	2,010 ms	Slowest single search (2 seconds)

5.4 Write (Create) Latency Breakdown

Percentile	Time
Minimum	21.72 ms
Average	264.10 ms
Median	114.30 ms
p(90)	700.78 ms
p(95)	1,000 ms
Maximum	2,680 ms

5.5 Threshold Results

Threshold	Target	Actual	Status
p(95) search < 600ms	600ms	651.65ms	FAILED
p(99) search < 1000ms	1,000ms	1,130ms	FAILED
Success rate > 95%	95%	100%	PASSED
Error rate < 5%	5%	0%	PASSED

5.6 Behaviour Under Load

ParadeDB started reasonably well at low user counts but began slowing down noticeably as users climbed past 50. At 100 concurrent users, some queries exceeded 2 seconds. This is because ParadeDB uses the same database engine (PostgreSQL) for both storing data AND searching — these two operations compete for the same CPU and memory when load is high.

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6. Elasticsearch — Detailed Results

6.1 Summary

Elasticsearch completed 5,041 search iterations with zero errors and passed all performance thresholds comfortably.

6.2 Core Metrics

Metric	Value
Total Iterations	5,041
Total HTTP Requests	10,082
Throughput	55.74 requests/second
Error Rate	0.00%
Success Rate	100.00%
Data Received	30 MB (168 kB/s)
Data Sent	1.4 MB (7.7 kB/s)

6.3 Search Latency Breakdown

Percentile	Time	Notes
Minimum	10.01 ms	Fastest single search
Average	33.48 ms	Typical search time
Median (p50)	22.25 ms	Half of searches faster than this
p(90)	57.65 ms	90% of searches faster than this
p(95)	101.23 ms	PASSED — well within 600ms
p(99)	198.96 ms	PASSED — well within 1,000ms
Maximum	405.62 ms	Slowest single search

6.4 Write (Create) Latency Breakdown

Percentile	Time
Minimum	18.78 ms
Average	47.23 ms
Median	32.92 ms
p(90)	85.77 ms
p(95)	123.52 ms
Maximum	469.90 ms

6.5 Threshold Results

Threshold	Target	Actual	Status
p(95) search < 600ms	600ms	101.23ms	PASSED
p(99) search < 1000ms	1,000ms	198.96ms	PASSED
Success rate > 95%	95%	100%	PASSED
Error rate < 5%	5%	0%	PASSED

6.6 Behaviour Under Load

Elasticsearch remained consistent throughout all load stages. Even at 100 concurrent users, the search latency barely moved — a hallmark of a system specifically engineered for search. The inverted-index architecture means searches never compete with writes for core resources.

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7. Head-to-Head Comparison

7.1 Search Speed

Metric	ParadeDB	Elasticsearch	ES Advantage
Average	194.85 ms	33.48 ms	5.8x faster
Median	117.49 ms	22.25 ms	5.3x faster
p(90)	441.42 ms	57.65 ms	7.7x faster
p(95)	651.65 ms	101.23 ms	6.4x faster
p(99)	1,130 ms	198.96 ms	5.7x faster
Maximum	2,010 ms	405.62 ms	5.0x faster

7.2 Write Speed

Metric	ParadeDB	Elasticsearch	ES Advantage
Average	264.10 ms	47.23 ms	5.6x faster
Median	114.30 ms	32.92 ms	3.5x faster
p(95)	1,000 ms	123.52 ms	8.1x faster
Maximum	2,680 ms	469.90 ms	5.7x faster

7.3 Throughput

Engine	Requests/Second	Iterations Completed	Data Received
ParadeDB	44.94	4,058	122 kB/s
Elasticsearch	55.74	5,041	168 kB/s
Difference	+24% ES	+24% ES	+38% ES

7.4 Reliability

Metric	ParadeDB	Elasticsearch
Error Rate	0.00%	0.00%
Failed Checks	0 / 12,174	0 / 15,123
Success Rate	100%	100%

Both engines are equally reliable — zero errors recorded. The difference is speed, not stability.

7.5 Overall Scorecard

Category	ParadeDB	Elasticsearch
Search Speed	2/5	5/5
Write Speed	2/5	5/5
Throughput	3/5	4/5
Reliability	5/5	5/5
Threshold Compliance	2/4	4/4
Operational Simplicity	5/5	2/5
Cost	5/5	3/5
Overall	24/35	28/35

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8. What the Numbers Mean (Plain English)

Is 195ms fast or slow?

Human perception guidelines from the UX industry:

Response Time	User Experience
Under 100ms	Feels instant — like flipping a light switch
100 – 300ms	Fast — user barely notices
300 – 1,000ms	Noticeable delay — user waits
Over 1,000ms	Frustrating — user thinks something is broken

Engine	Average Search	User Experience
Elasticsearch	33ms	Feels instant
ParadeDB	195ms	Noticeable but acceptable
ParadeDB at p(95)	652ms	Clearly slow to users
ParadeDB at p(99)	1,130ms	Frustrating experience

What does “100 virtual users” mean in the real world?

100 simultaneous virtual users roughly corresponds to a system under moderate business load — imagine 100 employees or customers all clicking “Search” at the same moment. For a banking or financial application, this is a realistic mid-day peak scenario.

Why did ParadeDB slow down but Elasticsearch didn’t?

ParadeDB uses PostgreSQL’s internal processes for search. When many users search simultaneously, PostgreSQL must divide its CPU, memory, and disk I/O between search queries, writing new records, and maintaining indexes. This competition for shared resources causes latency to grow under load.

Elasticsearch is a dedicated search engine. Its entire architecture is built around answering many simultaneous search queries fast. It uses a technique called an inverted index — think of it like the index at the back of a textbook — which allows it to find results almost instantly regardless of how many users are searching at the same time.

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9. Infrastructure & Operational Comparison

9.1 Architecture Complexity

Factor	ParadeDB	Elasticsearch
Number of services to run	1 (PostgreSQL only)	2 (PostgreSQL + Elasticsearch)
Data synchronisation needed	No	Yes — must sync all writes
Risk of data being out of sync	None	Yes — if sync fails, search is stale
Setup complexity	Low	Medium
Maintenance burden	Low	Medium-High

9.2 Data Consistency Risk

With Elasticsearch, every time a record is created, updated, or deleted in the main database, the application must also update Elasticsearch. If this sync step fails:

• The database has correct, up-to-date data
• Elasticsearch has stale or wrong data
• Search results could return outdated information

During this POC, Elasticsearch returned 0 results in initial testing due to a data synchronisation gap. This is a real operational risk that requires careful engineering to manage.

ParadeDB has no synchronisation risk — there is only one system to maintain.

9.3 Team Skill Requirements

Requirement	ParadeDB	Elasticsearch
Database expertise needed	Standard PostgreSQL skills	PostgreSQL + Elasticsearch admin
Monitoring	Standard DB monitoring	DB monitoring + ES cluster monitoring
Backup strategy	Single database backup	Database backup + ES snapshots
Scaling approach	Scale DB vertically	Scale ES cluster independently

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10. Cost Analysis

10.1 Infrastructure Cost (Cloud Estimate)

Item	ParadeDB	Elasticsearch
Main database server	Required	Required
Search service	Not needed	Additional instance ($100–$400/month)
Licensing	Free (open source)	Free (basic) / Paid (advanced)
Additional DevOps time	Minimal	Moderate

10.2 Summary

• ParadeDB is the lower cost option — no additional infrastructure
• Elasticsearch adds meaningful infrastructure cost but delivers significantly better performance
• For a production banking system serving hundreds of users, the performance gains of Elasticsearch typically justify the additional cost

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11. Risk Assessment

11.1 ParadeDB Risks

Risk	Likelihood	Impact	Mitigation
Slow search under high load	High (observed)	Medium	Caching, index tuning
BM25 index limitations at scale	Medium	Medium	Test with full production data volumes
Smaller community/support	Low	Medium	Monitor project maturity

11.2 Elasticsearch Risks

Risk	Likelihood	Impact	Mitigation
Data sync failure	Medium	High	Retry logic, message queues, monitoring
Increased operational complexity	Certain	Medium	DevOps training, documentation
Higher infrastructure cost	Certain	Low-Medium	Budget planning
Stale search results during outages	Medium	Medium	Monitoring, sync lag alerts

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12. Recommendation

Recommended: Elasticsearch

Based on the load test results and overall analysis, Elasticsearch is the recommended search technology for production use.

Reasons

1. Performance is decisive. Elasticsearch is 5–8x faster than ParadeDB across every metric measured. At 100 concurrent users, ParadeDB’s 95th-percentile response exceeded the acceptable threshold. Elasticsearch sat comfortably at 101ms — with enormous headroom for growth.

2. Both are equally reliable. Neither system produced a single error in the entire test. So reliability is not a differentiator — speed is the deciding factor.

3. Elasticsearch scales with your business. As user counts grow from 100 to 500 to 1,000+, Elasticsearch is designed to scale horizontally by adding more nodes. ParadeDB performance would continue to degrade under increasing load.

4. Industry-proven at scale. Elasticsearch powers search for Netflix, GitHub, LinkedIn, Wikipedia, and thousands of enterprise systems worldwide. It is battle-tested at scales far beyond what this application requires.

When ParadeDB Would Be Better

ParadeDB is the right choice if:

• The application has low search traffic (under 20–30 concurrent users)
• Operational simplicity is more important than raw performance
• The team lacks capacity to maintain two separate services
• Budget is very tight and performance at current scale is acceptable
• You want a quick MVP without extra infrastructure complexity

Adoption Roadmap

Phase	Action
Immediate	Use Elasticsearch for the production search implementation
Short-term	Implement robust sync (retry queues, dead-letter handling, error alerts)
Medium-term	Add Kibana dashboards for real-time search monitoring
Long-term	Re-evaluate ParadeDB in 12–18 months as it matures

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13. Appendix — Technical Details

13.1 Test Script Configuration (Both Tests)

stages: [
  { duration: "30s", target: 10  },  // warm-up
  { duration: "60s", target: 50  },  // ramp-up
  { duration: "60s", target: 100 },  // peak load
  { duration: "30s", target: 0   },  // cool-down
]

13.2 Application Endpoints Tested

Endpoint	Method	Purpose
/bank	POST	Create a new bank record
/search-hybrid?q={term}&useElastic=false	GET	Search via ParadeDB BM25
/search-hybrid?q={term}&useElastic=true	GET	Search via Elasticsearch

13.3 ParadeDB BM25 Index Definition

CREATE INDEX bank_search_idx
ON bank
USING bm25 ("Name", "Active")
WITH (key_field='Id');

13.4 Docker Container Configuration

Container	Image	Port
paradedb	paradedb/paradedb	5435
elasticsearch	elasticsearch:8.13.0	9200

13.5 Raw k6 Output Summary

ParadeDB:

create_latency.......: avg=264.1ms  min=21.72ms med=114.3ms  max=2.68s  p(90)=700.78ms p(95)=1s
parade_search_latency: avg=194.85ms min=15.61ms med=117.49ms max=2.01s  p(90)=441.42ms p(95)=651.65ms
http_req_failed......: 0.00%   0 out of 8116
http_reqs............: 8116    44.94/s
iterations...........: 4058    22.47/s

Elasticsearch:

create_latency.......: avg=47.23ms min=18.78ms med=32.92ms max=469.9ms  p(90)=85.77ms  p(95)=123.52ms
es_search_latency....: avg=33.48ms min=10.01ms med=22.25ms max=405.62ms p(90)=57.65ms  p(95)=101.23ms
http_req_failed......: 0.00%   0 out of 10082
http_reqs............: 10082   55.74/s
iterations...........: 5041    27.87/s

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Report generated from real k6 load test executions on 22 April 2026. Test scripts: k6-test/paradedb-load-test.js | k6-test/elasticsearch-load-test.js Raw results: k6-test/paradedb-results.json | k6-test/elasticsearch-results.json