Clixer, AI Powered Reporting & Decision Support Platform
TR Request a Demo

← Why Clixer?

PERFORMANCE

Performance: 1.7 billion rows, 2.00 seconds

In Clixer, speed is not an optimisation, it is an architectural decision. In a real enterprise scenario, the vendor's own BI tool rendered a 1.7-billion-row Oracle dataset in about 4 hours; Clixer put the same data on screen in 2.00 seconds. The difference: 7,200×.

Different DB Architecture7,200× BenchmarkMillisecond-Class
ARCHITECTURAL EDGE

Speed is a design decision, not a feature

Clixer doesn't follow the classic 'send the query to the source and wait' model; it uses its own data engine designed for reporting. The difference is measured in milliseconds on small data, and in hours on big data.

THE BENCHMARK

1.7 billion rows: 4 hours vs 2.00 seconds

Same Oracle data, same query, end-to-end timing (query + render). The vendor's own BI tool: ~4 hours; Clixer: 2.00 seconds. Taking 14,400 seconds down to 2: 7,200×.

AT EVERY SCALE

The same reflex at 10,000 rows

On a small query a typical competitor sits at ~10 seconds while Clixer answers in 0.19. What it means for the user is simple: change a filter and you don't wait, your analysis flow never breaks.

A dashboard that makes you wait is a dashboard nobody opens. A report with a 4-hour render can never be a daily decision tool; the team saves it for the weekly meeting, and decisions drift away from the data. That is what Clixer's performance claim is really about: data should flow at the speed of decisions.

The benchmark setup is transparent: the same 1.7-billion-row Oracle dataset, the same query, measured end to end, the time the user actually waits: query + render. The vendor's own BI tool finished in about 4 hours; Clixer in 2.00 seconds. 14,400 seconds / 2.00 seconds = 7,200×.

This is not a hardware trick; it is an architectural choice: Clixer doesn't pile reporting load onto the source system, it reports from its own engine, designed for exactly this job. The side effect is valuable too: your Oracle keeps doing its operational work; reporting never exhausts the source.

And the gap widens with scale: when data grows 100×, the wait doesn't. A 0.19-versus-10-seconds difference on small data becomes seconds versus hours in the billion-row class. The bigger you get, the further ahead Clixer pulls.

Highlights

  • Benchmark measured on real Oracle data, 1.7 billion rows: 4 hours → 2.00 seconds (7,200×)
  • 10,000 rows in 0.19 sec, vs ~10 sec on a typical competitor
  • End-to-end timing: query + render, the wait the user actually experiences
  • The source system is never exhausted: your Oracle keeps its operational load
  • The gap grows with scale: the bigger the data, the wider the lead
  • Millisecond-class interaction: filter, drill, detail, the flow never breaks

The benchmark was measured end to end on a real enterprise dataset; results vary with data model and environment. The healthiest test: let's measure on your own data.

Performance FAQs

Clixer uses a columnar engine designed for analytics (ClickHouse) plus smart caching. Sub-second query response on datasets of millions of rows is proven in live customer deployments. As data grows, the architecture scales horizontally.

No. The read layer works through the cache; response times hold even when every manager opens the same cockpit on Monday morning.

Classic BI tools query general-purpose databases or try to squeeze data into memory. Clixer uses a columnar OLAP engine built for analytical queries plus a Redis cache layer. The architectural difference is felt as a waiting-time difference.

Our live deployments work with tables of hundreds of millions of rows. The ClickHouse architecture scales to billions; the limit is the hardware you choose, not the software.

You hold the stopwatch: let's measure on your data

In a demo, we run the benchmark together on your own Oracle/ERP data, you keep the time.

Request a Demo