A community user at Zhihu.com (the Quora of China) shared a detailed TiDB case study:
Zhihu is the Quora of China with 220 million registered users, and it has 30 million questions with more than 130 million answers. With approximately 100 billion rows of data accruing each month and growing, this number will reach 3 trillion in two years. They can foresee great challenges in scaling their backend system while maintaining good user experience. With TiDB, they managed to harness the power of data and ensure good user experience because they get 99th percentile response time over these 1.3 trillion rows of data was about 25 ms, and the 999th percentile response time was about 50 ms.
The case study shows why they chose TiDB, how they are using it, what they learned and best practice, and some thoughts about the future with TiDB.
Full story is here:
tikv:
parser:
rust-prometheus:
TiDB:
TiKV:
TiDB:
Last week, we landed 55 PRs in the TiDB repository.
TiSpark:
TiKV and PD:
Last week, we landed 29 PRs in the TiKV and PD repositories.
0
config-check
command-line optionTiDB:
nulls
in Column
modify column
operation on the bit
columnindex_lookup_merge_join
in the physical planIndexNestedLoopHashJoin
point get
uses maxUint64
as its transaction timestampAVG
to COUNT
and SUM
for the TableReader
Coprocessor taskTiKV and PD:
approximate keys
function when the result is 0
txn_heart_beat
APIconfig-check
flag for tikv-serverconfig-check
flag for pd-serverDuration
in codec
TiDB: