qb VS tidb

While viewing FusionReactor logs for a ColdFusion app, I noticed lots of repetitive JDBC entries in a request that contained the same exact SQL statement and was taking 42ms per execution. I checked the UDF that was performing the query (using QB) and the query was configured to be cached for 5 minutes... but overhead-wise, it was still taking 42ms each time. The "Number of Queries" for the request was 57 and most of the queries were similar cached 40ms lookups and it started adding up to 1,452ms overall. I wondered if there was anything I could do to add self-contained caching to UDFs that could benefit from them. I didn't want to save the response to the session (YIKES! I've seen some code that does this) or use cachePut/cacheGet since the caching only needs to live for a single request of "repetitive access".

tidb has been around for a while, it is distributed, written in Go and Rust, and MySQL compatible. https://github.com/pingcap/tidb

Somewhat relatedly, StarRocks is also MySQL compatible, written in Java and C++, but it's tackling OLAP use-cases. https://github.com/StarRocks/starrocks

It's MySQL-Compatible database for scale and real-time analytics https://github.com/pingcap/tidb

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We work on a MySQL compatible distributed database called TiDB https://github.com/pingcap/tidb/ and key-value store called TiKV.

TiDB is written in Go and TiKV is written in Rust.

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Pesquise por CockroachDB ou TiDB

OLTP usually comes with high throughput of transactions, which means usually write(e.g., IUD - insert, update, delete) to read (e.g., select) ratio is above 4 or 5 or even higher. There are some good benchmarks to test OLTP workload like TPC-C (https://www.tpc.org/tpcc/), and some benchmarks to test OLAP workload like TPC-H (https://www.tpc.org/tpch/). For mixed or hybrid OLTP and OLAP (it's called HTAP, see this blog for some background https://en.pingcap.com/blog/the-beauty-of-htap-tidb-and-allo...), TPC-H was originally designed for this, however, it actually doesn't reveal the real world workload with several drawbacks. A newer research work from UC Berkeley proposed a HTAP benchmark called TAOBench (https://www.vldb.org/pvldb/vol15/p1965-cheng.pdf) which is pretty interesting and worthy to check.

For the HTAP systems, as mentioned in the above blog, there are quite a few industrial products, like Google just announced AlloyDB (https://cloud.google.com/alloydb), Snowflake's UniStore (https://www.snowflake.com/workloads/unistore/), and one of the most popular open source projects TiDB (https://github.com/pingcap/tidb) which have been deployed by many business applications.

Hopefully these may help a little bit :-)