GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. In We could know that the data of ClickHouse is stored within the distributed storage directly, which is very similar with EBS of AWS, even without remarkable performance degrading.
I am very suspicious of such a conclusion, as a result, has any one made any benchmark on similar solution as Ceph? Which means that to use a distributed storage either object storage or block to directly serve as the data layer of ClickHouse.
Additionally, according to the roadmapI also notice that a VFS would be provided such that ClickHouse could run on different distributed storages as S3 or HDFS, what's the anticipated performance degrading for such design? It is indeed slower than on local SSD but I don't remember the exact numbers need to check it again. But I don't know the exact performance numbers. This setup works but it's not near to be practical.
It is expected to be slower from fundamental perspective: if you need to keep up with sequential throughput of a single local NVMe SSD, you need at least 25 Gbit network bandwidth per each server node. But the setup can work as fast as with local storages if the cold storage will be actually cold. That means data should be moved there manually, after that ClickHouse can read it. We've made some experiments using Ceph as a Block device.
I'm not sure whether such features disk page cache contains object from remote DFS either block or object storage could exist for general cloud solutions, it seems to have ClickHouse run on such solution could be a good alternative for high availability.
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Sign up. New issue. Jump to bottom. Labels performance question question-answered. Copy link Quote reply. We are going to have native support for S3 as storage. There are two proposed use cases: store all data on S3 and pay for computation resources on demand pay for query ; store fresh data on local storage and cold data on S3 as a cold storage.
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Already on GitHub? Sign in to your account. While trying to benchmark clickhouse using SSB, I stumbled upon this 2 performance issues. Lets Assume the following query A:. This query A run for 30 seconds on a one terabyte DB. Then this A1 runs for only 7 seconds. So it means that Click preform the grouping and aggregation on 41 million rows for more than 20 seconds while querying over the fact lineorder and joining it took 7 seconds. Its seems like a very slow grouping.
Just to be on the safe side I have inserted A1 into temporary table T1 and run the group by and aggregation over it.
This query run for less than 1 second :. As you can see A2 suppose to run faster since its omitting the join preformed in A1. But it runs slower.Master do file stata
The join itself dose not change to row count since it was already done in the "In select" statement in the filter of A2. Can you advise? In order for the benchmark queries to run they've been rewritten to overcome the lack of support for implicit joins and multiple joins, into nested subqueries, the original query from which I rewrite A is:. As you can see, in Addition, I've dropped the Date dimension table, add a date column to the fact table and use date-time functions.
Cluster: I used 2 node cluster each node has 16 cores and 32G memory c5d. The queries run on a distribute fact table but local dimension tables present in full on each node of the cluster. This issue might be duplicate of issue In not sure thoughsince in it seems like a join related issue, while in my case the join statement is not slowing the query it strangely speeding it up.
ClickHouse tries not to read unused columns. But if the right table is small it could be some performance fluctuation. What is your right joined table size and what is the difference in median time of A1 and A2? Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Sign up.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. We need to improve kafka consuming performance, degraded after which is very important and desired change in all other aspects.
Since data consistency is more important and we are really happy with progress in kafka support in CH in last 2 months we postponed this question we didn't know if there was any bigger change in our virtual machines provider or if old version was simply missing some data. Most probably both options will not really help. Has anyone benchmarked what difference does a make? Would move to sharding help would each shard subsribe only to 1 partition or would first shard try to catch all partitions?
Will try to solve it in Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. Kafka: performance regression in Labels comp-kafka performance st-in-progress.
Copy link Quote reply. We have seen big drop in performance after this upgrade Since data consistency is more important and we are really happy with progress in kafka support in CH in last 2 months we postponed this question we didn't know if there was any bigger change in our virtual machines provider or if old version was simply missing some data However.Uber distribution channels
Collaborator Author. Bit better, but not very inspiring for now: Do you have any updates on this issue? KochetovNicolai added a commit that referenced this issue Dec 17, Kafka fixes backport Reopening port during reset.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
Already on GitHub? Sign in to your account. DB::Exception: Memory limit for query exceeded: would use Elapsed: 7. Processed OrderCreateTime, c. CityName, buser. BizUserName, sku. FirstDisPlayCategory, sku. Brand, soi. ProductName, sku. UnitPriceClass, so. OrderType, soi. ID inner join bizuser buser ON so. The dataset i store in clickhouse 36G, Original data size is about G.
The Mache total Memory is 48G. And I how to avoid this error message? I need to change some settings?This is huge. It seems to me that it's similar to BigQuery but has many other features that I didn't see in other databases. AggregatingMergeTree is especially one of them and allows incremental aggregation which is a huge gain for analytics services.
Also it provides many table engines for different use-cases. You don't even need a commit-log such as Apache Kafka in front of ClickHouse, you can just push the data to TinyLog table and and move data in micro-batches to a more efficient column-oriented table that uses different table engine.
But the more I look into the distributed storage section, the more corners I see cut in the data consistency section. You can't write data with confirmation that it was received by more than one replica.68rfe wont shift into 6th
If you write a batch of data to one replica and the server with this data ceases to exist before the data has time to get to the other replicas, this data will be lost. This only works for the last blocks inserted in a table. If the file sizes match but bytes have been changed somewhere in the middle, this is not detected immediately, but only when attempting to read the data for a SELECT query. The nature of clickstream data makes it somewhat okay to lose a few chunks in transit - I can imagine at least a few of the beacons will get dropped purely over the HTTP mechanism which pumps data into the system.
At some point, the data consistency costs money, slows down inserts and creates all sorts of limitations on how recovery of data would play out. But as a general purpose replicated DBMS which serves as a system of record against fraud allegations for instanceI can't see this comparing well. For those who are wondering: Doesn't build under osx, tho the issues initially don't see insurmountable, more that it hasn't been tried under CLANG. This makes me think they aren't lying when they say it supports linux in that they likely haven't tried building it on mac much.
That said it doesn't see to be using any crazy deps that don't support multiple platforms. Poco above is fully cross platform. Note per apparent comments below: Lots of people develop or use macs and so they'd be interested if they'd have to have a VM or other option to use this. Since the readme is super thin and it just says Only linux xxx I felt they didn't have much info.
I'm used to the days where people built projects that compiled everywhere but didn't build packages for them for some reason. I can read as well. Neither your comment nor their website say how far it might or might not be from running on another platform. My comment was merely a initial analysis for how far it might be from working on a mac which is important to a lot of people in here. Seeing that it's not working due to clang testing shows that they likely haven't tried on mac much.
No just default build. I found this  much more informative in terms of what this is good for. Reference is way more better than the actual page. This looked really interesting and then saw this: "This is not a cross-platform system.Following my post from a year ago about ClickHouse I wanted to review what happened in ClickHouse since then.
There is indeed some interesting news to share. It did not quite get into the topbut the gain from position to is still impressive. ClickHouse changed their versioning schema. Unfortunately, it changed from the unconventional …; 1.
ClickHouse: Two Years!
Support of the more traditional JOIN syntax. This has probably been the most requested feature since the first ClickHouse release. Updating or deleting rows in ClickHouse should be an exceptional operation, rather than a part of your day-to-day workload. It is still experimental, but I hope soon it will be production ready.
Basically it allows internally to replace long strings with a short list of enumerated values. How does this help? Firstly, it offers space savings. The table will take less space in storage, as it will use integer values instead of strings. And secondly, performance. The filtering operation will be executed faster.
Unfortunately this feature is not optimized for all use cases, and actually in aggregation it performs slower. It may not seem significant, but Tableau is the number one software for data analysts, and by supporting this, ClickHouse will reach a much wider audience. Vadim Tkachenko.Generalized quanti ers in categorial grammar
Percona Labs designs no-gimmick tests of hardware, filesystems, storage engines, and databases that surpass the standard performance and functionality scenario benchmarks. In short — RocksDB was designed as a key-value engine, while ClickHouse was designed for the bulk operations. With this RocksDB performance will be affected if there are multiple updates of a row that are not merged — RockDB will need to find the latest version of the row.
Vadim, thank you for the update, I am a newcomer and an early adopter of Clickhouse. Speaking about updates and deletes, in my opinion there should never be the case to implement them in the classic old-fashioned way of RDBMS. Both operations must be implemented with inserts, i.
ClickHouse: Two Years! Now to the more interesting technical improvements. FROM lineorder. FROM customer. Elapsed: 0. Processed Elapsed : 0. Elapsed: 1. Elapsed : 1. Elapsed: 2. Elapsed : 2.
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I need to get all the rows from order that for the same clientid on the same date have opposing type values. Keep in mind type can only have one of two values - B or S. In the example above this would be rows 23 and The other constraint is that the corresponding row in processed must be true for the orderid. This has piqued my interest and I'd like to know why.
The primary keys and respective foreign key columns are indexed while the value columns valueprocessed etc aren't. Disclaimer: I have inherited this DB structure and the performance difference is roughly 6 seconds. The reason that you're seeing a difference is due to the execution plan that the planner is putting together, this is obviously different depending on the query arguably, it should be optimising the 2 queries to be the same and this may be a bug.
This means that the planner thinks it has to work in a particular way to get to the result in each statement. When you do it within the JOIN, the planner will probably have to select from the table, filter by the "True" part, then join the result sets.
I would imagine this is a large table, and therefore a lot of data to look through, and it can't use the indexes as efficiently. I suspect that if you do it in a WHERE clause, the planner is choosing a route that is more efficient ie. You could probably make the join work as fast if not faster by adding an index on the two columns not sure if included columns and multiple column indexes are supported on Postgres yet.
In short, the planner is the problem it is choosing 2 different routes to get to the result sets, and one of those is not as efficient as the other.
If you want specifics on why your specific query is doing this, you'll need to provide more information. However the reason is the planner choosing different routes. Just skimmed, seems that the postgres planner doesn't re-order joins to optimise it. Learn more.
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