tingfirst

Makes sense if you prefer scanning the whole picture first. Whole-to-parts vs. parts-to-whole is a different mindset. I personally like fade-in in some cases—it helps me focus on one layer at a time and build up the full context without getting overwhelmed. That said, efficiency can be an issue, so each viz is situational and easy to overuse :-)

All streaming processors face the same fundamental problem:

Streaming joins require maintaining state for both sides of the join

High-cardinality data (millions of unique keys) means huge state sizes

Traditional approach: Keep everything in memory will make memory exhausted

The high-cardinality join memory problem isn't unique to Timeplus. Apache Flink also uses hybrid hash joins that spill to disk (RocksDB) when memory fills, Materialize shares indexed state across multiple queries (but still requires keeping full datasets in memory), and RisingWave stores state in cloud object storage (S3/GCS) with LRU caching for hot data. What makes Timeplus different is its purpose-built optimization for the Pareto Principle, where a tiny fraction of data generates the vast majority of activity - keeping hot data in memory and cold data on disk for dramatic memory savings.

Redpanda + Timeplus, the perfect pair for data streaming developers. No JVM, ZK ...

Probably the smallest yet most powerful binary for real-time, incremental SQL data processing, end to end!

Consistently we heard about ClickHouse has very limited materialized views that can't handle real-time pipeline fast efficiently enough. would love to see more comments here.

Data sources are usually in Kafka, or other operational databases like Postgres or MySQL

1. Table A : fact events, high-throughput (10k~1M eps), high-cardinality

2. Table B, C, D : couple of dimension tables (fast or slow changing).

The use case is straightforward : join/enrich/lookup everything into one big flattened, analytics-friendly table into ClickHouse.

What’s the best pipeline approach to achieve this in real-time and efficiently?

For parallel programming, what's OS-level difference compared to languages like Python or modern C++?

Domain.spawn (fun _ -> print_endline "I ran in parallel")

Anyway, love the simplicity of this expression!

AI can be hallucination but real-time detection is key

re EPS and CPU utilization, WS still performs better than SSE?