I read that the implementation makes certain assumptions like the write optimization, mostly append writes, sequential deletes, but what is there in it's implementation that makes a time series database better suited for storing a time series than a mysql with proper indexing and relational structure.
我读到实现做了一些假设，比如写优化，主要是附加写，顺序删除，但是它的实现中有什么使得时间序列数据库更适合存储时间序列而不是具有适当索引和关系结构的mysql。
2 个解决方案
#1
0
The question is very generic, but here's a fairly detailed list of the tradeoffs that InfluxDB makes, and why they allow for greater performance on time-series data.
这个问题非常通用，但这里有一个相当详细的关于InfluxDB所做出的权衡的列表，以及为什么它们允许在时间序列数据上获得更好的性能。
https://docs.influxdata.com/influxdb/v0.10/concepts/insights_tradeoffs/
https://docs.influxdata.com/influxdb/v0.10/concepts/insights_tradeoffs/
Even tweaked to the edge of maintainability, can you push 1 million records per second into MySQL for weeks on end without it falling over? How can you bulk delete the old data that's no longer relevant while maintaining that ingest rate?
即使调整到可维护性的边缘，你可以连续数周将每秒100万条记录推送到MySQL而不会摔倒吗？如何在保持摄取率的同时批量删除不再相关的旧数据？
#2
0
Time series databases store observations in a highly optimized format. This means faster reads, faster writes along with flexible schema. For instance, compare storage requirements in Axibase Time Series Database (my affiliation) vs MySQL for sensor readings. 7 bytes ATSD vs 63 bytes MySQL. http://axibase.com/products/axibase-time-series-database/data-storage-tests/atsd-vs-mysql/
时间序列数据库以高度优化的格式存储观察结果。这意味着更快的读取，更快的写入以及灵活的架构。例如，比较Axibase时间序列数据库（我的从属关系）与MySQL的存储要求，以获取传感器读数。 7字节ATSD与63字节MySQL。 http://axibase.com/products/axibase-time-series-database/data-storage-tests/atsd-vs-mysql/
#1
0
The question is very generic, but here's a fairly detailed list of the tradeoffs that InfluxDB makes, and why they allow for greater performance on time-series data.
这个问题非常通用，但这里有一个相当详细的关于InfluxDB所做出的权衡的列表，以及为什么它们允许在时间序列数据上获得更好的性能。
https://docs.influxdata.com/influxdb/v0.10/concepts/insights_tradeoffs/
https://docs.influxdata.com/influxdb/v0.10/concepts/insights_tradeoffs/
Even tweaked to the edge of maintainability, can you push 1 million records per second into MySQL for weeks on end without it falling over? How can you bulk delete the old data that's no longer relevant while maintaining that ingest rate?
即使调整到可维护性的边缘，你可以连续数周将每秒100万条记录推送到MySQL而不会摔倒吗？如何在保持摄取率的同时批量删除不再相关的旧数据？
#2
0
Time series databases store observations in a highly optimized format. This means faster reads, faster writes along with flexible schema. For instance, compare storage requirements in Axibase Time Series Database (my affiliation) vs MySQL for sensor readings. 7 bytes ATSD vs 63 bytes MySQL. http://axibase.com/products/axibase-time-series-database/data-storage-tests/atsd-vs-mysql/
时间序列数据库以高度优化的格式存储观察结果。这意味着更快的读取，更快的写入以及灵活的架构。例如，比较Axibase时间序列数据库（我的从属关系）与MySQL的存储要求，以获取传感器读数。 7字节ATSD与63字节MySQL。 http://axibase.com/products/axibase-time-series-database/data-storage-tests/atsd-vs-mysql/