Each event has an ID, event type, timestamp, and a JSON representation of event properties. Full text search? Even at 500-row batches, ClickHouse consumed 1.75x more disk space than TimescaleDB for a source data file that was 22GB in size.
Weve already established why ClickHouse is excellent for analytical workloads.
Instead, you want to pick an architecture that evolves and grows with you, not one that forces you to start all over when the data starts flowing from production applications.
Understanding ClickHouse, and then comparing it with PostgreSQL and TimescaleDB, made us appreciate that there is a lot of choice in todays database market - but often there is still only one right tool for the job. The SQL SELECT TOP Clause. PostgreSQL has the benefit for 20+ years of development and usage, which has resulted in not just a reliable database, but also a broad spectrum of rigorously tested tools: streaming replication for high availability and read-only replicas, pg_dump and pg_recovery for full database snapshots, pg_basebackup and log shipping / streaming for incremental backups and arbitrary point-in-time recovery, pgBackrest or WAL-E for continuous archiving to cloud storage, and robust COPY FROM and COPY TO tools for quickly importing/exporting data with a variety of formats. To avoid making this post even longer, we opted to provide a short comparison of the two databases - but if anyone wants to provide a more detailed comparison, we would love to read it.). ClickHouse primarily uses the MergeTree table engine as the basis for how data is written and combined.
A source can be a table in another database (ClickHouse, MySQL or generic ODBC), file, or web service. What our results didn't show is that queries that read from an uncompressed chunk (the most recent chunk) are 17x faster than ClickHouse, averaging 64ms per query. Here is a similar opinion shared on HackerNews by stingraycharles (whom we dont know, but stingraycharles if you are reading this - we love your username): "TimescaleDB has a great timeseries story, and an average data warehousing story; Clickhouse has a great data warehousing story, an average timeseries story, and a bit meh clustering story (YMMV).". There is at least one other problem with how distributed data is handled.
More importantly, this holds true for all data that is stored in ClickHouse, not just the large, analytical focused tables that store something like time-series data, but also the related metadata. ClickHouse chose early in its development to utilize SQL as the primary language for managing and querying data. As we've shown previously with other databases (InfluxDB and MongoDB), and as ClickHouse documents themselves, getting individual ordered values for items is not a use case for a MergeTree-like/OLAP database, generally because there is no ordered index that you can define for a time, key, and value. You signed in with another tab or window. Also note that if many joins are necessary because your schema is some variant of the star schema and you need to join dimension tables to the fact table, then in ClickHouse you should use the external dictionaries feature instead. 
Adding even more filters just slows down the query. The query looks like this in TimescaleDB: As you might guess, when the chunk is uncompressed, PostgreSQL indexes can be used to quickly order the data by time. Thank you! To that end, for comparing both insert and read latency performance, we used the following setup in AWS: ClickHouse: No configuration modification was done with the ClickHouse.
We'll go into a bit more detail below on why this might be, but this also wasn't completely unexpected. The sparse index makes ClickHouse not so efficient for point queries retrieving single rows by their keys. To overcome these limitations, ClickHouse implemented a series of vector algorithms for working with large arrays of data on a column-by-column basis. The payment table has data in the following columns: foreign key (student_id and course_code, the primary keys of the enrollment table), status, and amount. 
Yet every database is architected differently, and as a result, has different advantages and disadvantages. PostHog as an analytics tool allows users to slice and dice their data in many ways across huge time ranges and datasets. When selecting rows based on a threshold, TimescaleDB demonstrates between 249-357% the performance of ClickHouse when computing thresholds for a single device, but only 130-58% the performance of ClickHouse when computing thresholds for all devices for a random time window. Latencies in this chart are all shown as milliseconds, with an additional column showing the relative performance of TimescaleDB compared to ClickHouse (highlighted in green when TimescaleDB is faster, in blue when ClickHouse is faster). One last thing: you can join our Community Slack to ask questions, get advice, and connect with other developers (we are +7,000 and counting!). We help you build better products faster, without user data ever leaving your infrastructure. It's hard to find now where it has been fixed. It combines the best of PostgreSQL plus new capabilities that increase performance, reduce cost, and provide an overall better developer experience for time-series. 
of unique columns, This would complicate live data ingestion a lot, introducing new and exciting race conditions.
Here are some of the key aspects of their architecture: First, ClickHouse (like nearly all OLAP databases) is column-oriented (or columnar), meaning that data for the same table column is stored together. With larger batches of 5,000 rows/batch, ClickHouse consumed ~16GB of disk during the test, while TimescaleDB consumed ~19GB (both before compression). Want to host TimescaleDB yourself? Non SQL Server databases use keywords like LIMIT, OFFSET, and ROWNUM. (benchmarking, not benchmarketing). These architectural decisions also introduce limitations, especially when compared to PostgreSQL and TimescaleDB. In roadmap on Q4 of 2018 (but it's just a roadmap, not a hard schedule). ClickHouse was designed with the desire to have "online" query processing in a way that other OLAP databases hadn't been able to achieve. Do you notice something in the numbers above? As a product, we're only scratching the surface of what ClickHouse can do to power product analytics.
Subscribe to our With all these capabilities, PostgreSQL is quite flexible - which means that it is essentially future-proof. This is what the lastpoint and groupby-orderby-limit queries benchmark. Before compression, it's easy to see that TimescaleDB continually consumes the same amount of disk space regardless of the batch size.
var d = new Date() Clearly ClickHouse is designed with a very specific workload in mind.
At some point after this insert, ClickHouse will merge the changes, removing the two rows that cancel each other out on Sign, leaving the table with just this row: But remember, MergeTree operations are asynchronous and so queries can occur on data before something like the collapse operation has been performed. When we ran TimescaleDB without compression, ClickHouse did outperform. Tables are wide, meaning they contain a large number of columns. While it's understandable that time-series data, for example, is often insert-only (and rarely updated), business-centric metadata tables almost always have modifications and updates as time passes.
Just creating the column is not enough though, since old data queries would still resort to using a JSONExtract. Why ClickHouse didn't allow more than one JOIN in query? Again, the value here is that MergeTree tables provide really fast ingestion of data at the expense of transactions and simple concepts like UPDATE and DELETE in the way traditional applications would try to use a table like this. For this benchmark, we made a conscious decision to use cloud-based hardware configurations that were reasonable for a medium-sized workload typical of startups and growing businesses. If we wanted to query login page pageviews in August, the query would look like this: This query takes a while complete on a large test dataset, but without the URL filter the query is almost instant. In our benchmark, TimescaleDB demonstrates 156% the performance of ClickHouse when aggregating 8 metrics across 4000 devices, and 164% when aggregating 8 metrics across 10,000 devices. It will include not only the first expensive product but also the second one, and so on. If you want to host TimescaleDB yourself, you can do it completely for free - visit our GitHub to learn more about options, get installation instructions, and more ( are very much appreciated! As a result, all of the advantages for PostgreSQL also apply to TimescaleDB, including versatility and reliability. In practice, ClickHouse compresses data well, making this a worthwhile trade-off. But nothing in databases comes for free - and as well show below, this architecture also creates significant limitations for ClickHouse, making it slower for many types of time-series queries and some insert workloads. We've seen numerous recent blog posts about ClickHouse ingest performance, and since ClickHouse uses a different storage architecture and mechanism that doesn't include transaction support or ACID compliance, we generally expected it to be faster.
Generally, ClickHouse is known for its high insert rates, fast analytical queries, and SQL-like dialect. Do you know it is possible to multi join already, right? 2021 Timescale, Inc. All Rights Reserved. Lack of ability to modify or delete already inserted data with a high rate and low latency.
But TimescaleDB adds some critical capabilities that allow it to outperform for time-series data: Time-series data has exploded in popularity because the value of tracking and analyzing how things change over time has become evident in every industry: DevOps and IT monitoring, industrial manufacturing, financial trading and risk management, sensor data, ad tech, application eventing, smart home systems, autonomous vehicles, professional sports, and more. In the first part, we use the student_id column from the enrollment table and student_id from the payment table. If youd like to get data stored in tables joined by a compound key thats a primary key in one table and a foreign key in another table, simply use a join condition on multiple columns. PostgreSQL supports a variety of data types including arrays, JSON, and more. Regardless, the related business data that you may store in ClickHouse to do complex joins and deeper analysis is still in a MergeTree table (or variation of a MergeTree), and therefore, updates or deletes would still require an entire rewrite (through the use of `ALTER TABLE`) any time there are modifications.
The story does change a bit, however, when you consider that ClickHouse is designed to save every "transaction" of ingested rows as separate files (to be merged later using the MergeTree architecture). The SELECT TOP statement returns a specified number of records.
The key thing to understand is that ClickHouse only triggers off the left-most table in the join. If your query only needs to read a few columns, then reading that data is much faster (you dont need to read entire rows, just the columns), Storing columns of the same data type together leads to greater compressibility (although, as we have shown, it is possible to build. Since I'm a layman in database/ClickHouse. As an example, consider a common database design pattern where the most recent values of a sensor are stored alongside the long-term time-series table for fast lookup. Could your application benefit from the ability to search using trigrams? I think this is last important feature, that prevents In other words, data is filtered or aggregated, so the result fits in a single servers RAM. Some form of transaction support has been in discussion for some time and backups are in process and merged into the main branch of code, although it's not yet recommended for production use. There are batch deletes and updates available to clean up or modify data, for example, to comply with GDPR, but not for regular workloads. Other tables can supply data for transformations but the view will not react to inserts on those tables. So, let's see how both ClickHouse and TimescaleDB compare for time-series workloads using our standard TSBS benchmarks. And as a developer, you need to choose the right tool for your workload. For this case, we use a broad set of queries to mimic the most common query patterns. At Timescale, we take our benchmarks very seriously.
There is one large table per query. There is not currently a tool like timescaledb-tune for ClickHouse. For example, if # of rows in table A = 100 and # of rows in table B = 5, a CROSS JOIN between the 2 tables (A * B) would return 500 rows total. If your application doesn't fit within the architectural boundaries of ClickHouse (or TimescaleDB for that matter), you'll probably end up with a frustrating development experience, redoing a lot of work down the road. A query result is significantly smaller than the source data. Most of the time, a car will satisfy your needs. Temporary Tables ClickHouse supports temporary tables which have the following characteristics: Temporary tables disappear when the session ends, including if the connection is lost.
Multiple JOINs per SELECT are still not implemented yet, but they are next in queue of SQL compatibility tasks.
In the next condition, we get the course_code column from the enrollment table and course_code from the payment table. The typical solution would be to extract $current_url to a separate column. For reads, quite a large number of rows are processed from the DB, but only a small subset of columns.