tingfirst

4 ms·

1.
by tingfirst 7mo ago
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 get
2.
by tingfirst 8mo ago
Temporal binding is one of the hardest problems in visualization, all about aggregations across different time windows. And it gets even harder when the clock never stops and live data keeps flowing. Client visualization layer: Timeplus Vis
3.
Hybrid Hash Join – breaking the memory wall of streams join(timeplus.com)
2 points by tingfirst 11mo ago|1 comments
4.
by tingfirst 11mo ago
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
5.
by tingfirst 11mo ago
Redpanda + Timeplus, the perfect pair for data streaming developers. No JVM, ZK ...
6.
by tingfirst 11mo ago
Probably the smallest yet most powerful binary for real-time, incremental SQL data processing, end to end!
7.
by tingfirst 1y ago
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.
8.
by tingfirst 1y ago
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
9.
Any pipeline tool for ClickHouse, similar to Snowflake's Dynamic Tables(snowflake.com)
2 points by tingfirst 1y ago|6 comments
10.
by tingfirst 1y ago
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? [1] Dynamic Tables: One of Snowflake’s Fastest-Ad
11.
by tingfirst 1y ago
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!
12.
by tingfirst 1y ago
AI can be hallucination but real-time detection is key
13.
by tingfirst 1y ago
re EPS and CPU utilization, WS still performs better than SSE?
14.
OCaml for Kafka stream processing, analytics and telemetry data(github.com)
4 points by tingfirst 1y ago|3 comments
15.
by tingfirst 1y ago
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 Q
16.
by tingfirst 1y ago
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! [1] https:&#x2F
17.
Catching AI Hallucination in SQL: The Chess Example(timeplus.com)
3 points by tingfirst 1y ago|0 comments
18.
by tingfirst 2y ago
Pretty cool to see a C++ R/W Iceberg client without dependency, and even better open-sourced. The pipeline is all about processing and routing, ideally, to open and flexible destination with no lock-in and long-term retention. Writin
19.
by tingfirst 3y ago
Design principles of Timeplus Proton are simplicity, speed and efficiency. That's why we love ClickHouse, the fastest and most lightweight approach for real-time analytics. Furthermore, data stream processing should also uphold these s
20.
by tingfirst 3y ago
Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot. Please check this PR
21.
by tingfirst 3y ago
Redpanda Proton ClickHouse: A perfect match as a single-binary approach for a lightweight and high-performance data streaming processing and analytics in one compact box!
22.
by tingfirst 3y ago
Great points! SQLite as an analogy is fantastic for its small footprint. Swift download, deployment, and testing significantly boost dev productivity. Moreover, the dependency-free single-binary can efficiently slash deployment and operatio
23.
by tingfirst 3y ago
As a streaming SQL engine, Proton is a fast and lightweight alternative to Apache Flink, powered by ClickHouse. It can help developers solve the data streaming challenges from processing, routing to analytics, and send aggregated data to t