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.
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.
Is there a native SQL pipeline tool for ClickHouse that processes real-time data incrementally, with low latency, large throughput and high efficiency, similar to Snowflake’s Dynamic Tables?
For OCaml users interested in data streaming processing (similar to Flink or Spark), but looking for a faster and more efficient option, check out this OCaml plugin Timeplus Proton. Concise, safe, highly performant and fun
-> Streaming Queries - Process large datasets with constant memory usage
-> Async Inserts - High-throughput data ingestion with automatic batching
-> Compression - LZ4 and ZSTD support for reduced network overhead
-> TLS Security - Secure connections with certificate validation
-> Connection Pooling - Efficient resource management for high-concurrency applications
-> Rich Data Types - Full support for #ClickHouse types including Arrays, Maps, Enums, DateTime64
-> Idiomatic OCaml - Functional API leveraging OCaml's strengths
For OCaml users interested in data streaming processing (similar to Flink or Spark), but looking for a faster and more efficient option, check out this OCaml plugin Timeplus Proton. Concise, safe, highly performant and fun!