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That's 1-5 billion rows, per day, with 60 days of data, onto a single i5 3500 desktop I have laying around. It returns a complex query in less than 5 minutes.
I was gonna get a beef-ier server, but 5 minutes is fine for my task. I was flabbergasted.
I think it's an unfair comparison, notably because: 1) Clickhouse is rigid-schema + append-only - you can't simply dump semi-structured data (csv/json/documents) into it and worry about schema (index definition) + querying later. The only clickhouse integration I've seen up close had a lot of "json" blobs in it as a workaround, which cannot be queried with the same ease as in ES. 2) Clickhouse scalability is not as s…
Also it can cause performance problems if you have really heterogeneous data with lots of different fields https://www.elastic.co/guide/en/elasticsearch/reference/curr...
[1] https://www.elastic.co/guide/en/elasticsearch/reference/curr...
How does clickhouse compare to druid, pinot, rockset (commercial), memsql (commercial). I know clickhouse is easier to deploy. But from user's perspective is clickhouse superior to the others?
Anyone know more lightweight alternative to (ELK) Elastic Stack? I found https://vector.dev but it seems to be only the "L" part.
I am using Clickhouse at my workplace as a side project. I wrote a Rust app that dumps the daily traffic data collected from my company's products into a ClickHouse database. That's 1-5 billion rows, per day, with 60 days of data, onto a single i5 3500 desktop I have laying around. It returns a complex query in less than 5 minutes. I was gonna get a beef-ier server, but 5 minutes is fine for my task. I was flabbergas…
Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.
It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.
In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.
This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.
[1] https://cran.r-project.org/web/packages/sqldf/index.html
Sorry to hijack the thread but can anyone suggest alternatives to the 'search' side of Elasticsearch? I haven't been following the topic and there's probably new and interesting developments like ClickHouse is for logging.
ClickHouse is incredible. It has also replaced a large, expensive and slow Elasticsearch cluster at Contentsquare. We are actually starting an internal team to improve it and upstream patches, email me if interested!
Yep, do you guys have a writeup on this? Altinity actually mention Contentsquare case in their video, here: https://www.youtube.com/watch?t=2479&v=pZkKsfr8n3M&feature=y...
I think it's an unfair comparison, notably because: 1) Clickhouse is rigid-schema + append-only - you can't simply dump semi-structured data (csv/json/documents) into it and worry about schema (index definition) + querying later. The only clickhouse integration I've seen up close had a lot of "json" blobs in it as a workaround, which cannot be queried with the same ease as in ES. 2) Clickhouse scalability is not as s…
Unless you love rewrites, you can't simply dump semi-structured data into ElasticSearch either. Seen multiple apps with 5x or worse ES storage usage tied to 'data model' or lack thereof, and fixing it inevitably means revisiting every piece of code pushing stuff into and out of ES.
I love ES but this notion of schema free is dumb, in practice it's a nightmare.