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Could you name those good analytical databases? I'd love to learn more.
Snowflake, Redshift, BigQuery, Databricks, Presto.
Earlier quoted context omitted.
Could you name those good analytical databases? I'd love to learn more.
Snowflake, Redshift, BigQuery, Databricks, Presto.
BigQuery is much slower and is much more expensive for both storage and query.
Databricks (Spark) is even slower than that (both io and compute), although you can write custom code/use libs.
You seem to underestimate how heavily ClickHouse is optimized (e.g. compressed storage).
Earlier quoted context omitted.
Could you name those good analytical databases? I'd love to learn more.
Snowflake, Redshift, BigQuery, Databricks, Presto.
Earlier quoted context omitted.
Could you name those good analytical databases? I'd love to learn more.
Snowflake, Redshift, BigQuery, Databricks, Presto.
DataBricks is essentially Spark, and I shouldn’t need a whole spark cluster just to get database functionality. It also costs money.
Unless I’m mistaken, Presto is just a distributed query tool over the top of a separate storage layer, so that’s 2 things you have to setup.
I have no experience with BigQiery but I’ve heard good things about it and Redshift, however but if the rest of your infra isn’t on GCP/AWS then that will probably be a blocker.
Clickhouse is open source, comes with convenient clients in a bunch of languages as well as a HTTP API. It’s outrageously fast and has some cool features and makes the right trade-offs for its use-case, large range of supported input/output formats, built-in Kafka support and the replication and sharding is reasonably straightforward to setup.
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For analytics - sure. Clickhouse was not designed to handle OLTP workload, there's no transaction support.
I cringe a bit inside at people using say nosql approaches when it makes literally no sense to do so. Therefore I think the lack of OLTP will not matter much and that clickhouse will be widely used, but also misused when it becomes too fashionable.
If you need deletes and transactions, look elsewhere, but Clickhouse seems to be great for what it's been designed for.
https://blog.sentry.io/2019/05/16/introducing-snuba-sentrys-...
Earlier quoted context omitted.
For analytics - sure. Clickhouse was not designed to handle OLTP workload, there's no transaction support.
I cringe a bit inside at people using say nosql approaches when it makes literally no sense to do so. Therefore I think the lack of OLTP will not matter much and that clickhouse will be widely used, but also misused when it becomes too fashionable.
For example, aside from the lack of transactions, Clickhouse is designed for insertion. There's an INSERT statement, but no UPDATE or DELETE statements. You can rewrite tables (there's ALTER TABLE ... UPDATE and ALTER TABLE ... DELETE), but they're intended for large batch operations, and the operations potentially asynchronous, meaning that they complete right away, but you only see results later.
ClickHouse has many other limitations. For example, there's no enforcement of uniqueness: You can insert the same primary key multiple times. You can dedupe the data, but only specific table engines support this.
There's absolutely no way anyone will want to use ClickHouse as a general-purpose database.
Why do people on HN love Clickhouse so much? As far as I can tell, it’s an ordinary column store, with a bunch of limitations around distributed joins and a heuristic-based query planner. There are several good analytical databases that will give you the same scan performance and a much better query planner and executor. This is not a rhetorical question, I would really like to know why it gets so much attention here…
The closest open source thing that matches its feature set is Presto, but that one is quite different.
Apache Druid is supposed to be very mature, but also very difficult to set up and manage. I've not used it myself.
There's also Vespa, but I don't know how well it performs with large numbers of columns.
A lot of people use Elasticsearch for analytics. Being based on Lucene, it's kind of columnar, and it can perform very well indeed on aggregations.
InfluxDB may be good, but it's not fully open source.
I wonder how performant this is in comparison to other quick and dirty methods to work on local files like bash, pythonz etc.
There are some examples in the article cited by dang: https://news.ycombinator.com/item?id=20163017
Why do people on HN love Clickhouse so much? As far as I can tell, it’s an ordinary column store, with a bunch of limitations around distributed joins and a heuristic-based query planner. There are several good analytical databases that will give you the same scan performance and a much better query planner and executor. This is not a rhetorical question, I would really like to know why it gets so much attention here…
Because it's one of the few mature, fast, scalable analytics-oriented databases that is also completely open source. The closest open source thing that matches its feature set is Presto, but that one is quite different. Apache Druid is supposed to be very mature, but also very difficult to set up and manage. I've not used it myself. There's also Vespa, but I don't know how well it performs with large numbers of colum…