3 ms·

Most people shouldn't. Clickhouse or other column-store data warehouses (redshift, bigquery, etc) are very fast and have all the features to handle time-series

Most people shouldn't. Clickhouse or other column-store data warehouses (redshift, bigquery, etc) are very fast and have all the features to handle time-series and other data. Druid is good if you (1) make use of native integrations like Kafka (2) Need every field indexed for fast seeking to a few rows (3) can use the JSON API to make up for the still-in-beta SQL interface (4) don't need every single event/row as they are pre-aggregated (5) always have a time-column to partition by (6) want to use the native S3 tiering for older data (7) dont need joins and complex analysis Imply's distribution is better than core Druid but it's still more operationally complex than Clickhouse and alternatives.

leetbulb 7y ago
1) Most people handling large data streams are already using Kafka or similar. If you aren't, Druid's has pretty wide support for event ingress, including HTTP. 2) If you're looking to provide this type of analytics in the first place, you probably do want this. Being able to execute extremely fast, granular, ad-hoc queries over highly dimensional data is very powerful. I designed a reporting frontend that really took advantage of this and I always felt guilty when people would complimented me how fast it drilled down in to 10+ dimensions. 3) There are plenty of mature libraries for the Druid API. 4) This is almost inherent with any OLAP system. Although, even with high-cardinality data, Druid performs extremely well. Either way, you should have a backing data warehouse and offline, batch jobs if you need to perform BI analytics on row-by-row / non-aggregated data. Remember, OLAP sits on top of a data warehouse. 5) Yes, but technically you just need a key that increments. 6) Not sure if this is an argument against Druid. I've found the S3 deep storage / tiering to be very efficient and powerful, especially because you can create and push segments directly to storage and not run it through an indexer. S3 is also just an object store protocol specification now. There are lots of people who run S3-compatible object stores in-house. HDFS is also natively supported and another widely used storage backend in this space. Also, there are plenty of community extensions for other object stores. 7) Again, OLAP. Data flowing in to your OLAP layer should already be denormalized and ready for indexing. Also, you can join data in to your existing indices with Hadoop, etc. Druid support joins and lookups, although I've never used them. ClickHouse and other similar systems also don't do very well with joins. Maybe we have different definitions of "complex analysis," but in my experience, you can do some pretty crazy stuff with queries including writing your own JS functions, and if you're really dedicated, you can write your own extensions. One thing that I feel like a lot of people miss is that Druid is specifically an OLAP layer designed for large scale, and I mean beyond Netflix size scale (they use Druid). Every individual component of Druid is designed to scale out independently and play nicely with your existing infrastructure. Similar to ES, you have nodes that have specific roles such as event ingress / real-time compute, query handlers, query brokers, historical data / cache / compute, etc. Then you also have a bunch of supporting architecture provided for you for (re)indexing data, coordinating the entire cluster, etc. Druid is huge, not an AIO (OLAP, OLTP, DWH, etc) analytics solution, and it takes more than one person to run a larger cluster, even though I did it for a few years.
- manigandham 7y ago
  Relational databases with joins and full SQL support are still unmatched in flexibility, and functionality like materialized views, aggregation pipelines (and table engines for Clickhouse) allows you to do everything that Druid does with aggregated summaries while still having all the other querying abilities. Druid has a slight edge in data scale-out and indexed seeks but modern data warehouses are adding similar tiering features, along with with field indexing, full-text search, nested data records, and even in-memory rowstores for OLTP support. They're all converging on the same feature set and eventually Druid will just become another data warehouse option, although I'd still recommend Clickhouse or MemSQL at that point.
  - sanderjd 7y ago
    Question for everyone in this thread: what resources do you use to follow this stuff? Are there blogs or conferences or ... that I should follow if I'm interested in this topic?
    - manigandham 7y ago
      Distributed SQL data warehouses? Start with the papers: - C-Store (column-oriented database storage and processing): http://db.lcs.mit.edu/projects/cstore/vldb.pdf http://db.lcs.mit.edu/projects/cstore/vldb.pdf and https://sookocheff.com/post/databases/c-store/ https://sookocheff.com/post/databases/c-store/ - BigQuery (the original Dremel-based query engine): https://cloud.google.com/files/BigQueryTechnicalWP.pdf https://cloud.google.com/files/BigQueryTechnicalWP.pdf - Snowflake paper: https://www.snowflake.com/resource/sigmod-2016-paper-snowflake-elastic-data-warehouse/ https://www.snowflake.com/resource/sigmod-2016-paper-snowfla... - Andy Pavlo's CMU distributed OLAP databases lecture: https://www.youtube.com/watch?v=dPMc7FZ3Gqo&t=1s https://www.youtube.com/watch?v=dPMc7FZ3Gqo&t=1s - MemSQL architecture: https://www.youtube.com/watch?v=HoompXCG5Mk https://www.youtube.com/watch?v=HoompXCG5Mk Start there and just read more about architecture of the various products. You'll learn the basics of distributed nodes that store data in column-oriented tables partitioned by a key and how to run queries over them using various performance techniques. Then you can dive into the unique differences of the various databases.
DevKoala 7y ago
You are right about Clickhouse, but other data warehouses are not optimized for the same use case of Druid and Clickhouse, OLAP. For example, RedShift and BigQuery cannot be put behind a user facing backend. BigQuery has a default limit of 50 concurrent queries, if that's your user limit, perfect. RedShift takes seconds for queries that Druid and Clickhouse can answer in milliseconds.
- manigandham 7y ago
  All data warehouses are designed for OLAP, that's their purpose. It doesn't require low latency though. Redshift is an always running cluster of scale-out distributed postgres forked by AWS so it can and does return results in milliseconds, very similar to Clickhouse although still not as advanced in performance techniques. Bigquery is a completely managed model that uses far greater scale-out architecture designed for througput (petabytes in seconds) rather than latency, although it has real-time streaming, BI Engine (memory cache) and materialized views so you can get pretty close today. Snowflake is another option that runs on top of major clouds using instances to access object storage and also has low latency when your cluster is running.
  - DevKoala 7y ago
    Not all data warehouses are designed for OLAP. As the performance of these systems increase, it is possible to satisfy OLAP use cases with some warehouses such a BQ, but they are still not ideal as your number of concurrent queries scale and your response time requirements remain constant. The BQ team certainly doesn't consider their product an OLAP solution, neither the RedShift team. AWS is actually working on their own OLAP solution, Timestream; that is how they pitch it, as their OLAP solution. BigQuery's petabyte aggregations in seconds is a false claim. I just launched a query to count the number of records of a 1.4 PB dataset and it has taken over a minute to just schedule the query, I have 1000+ compute units available too. I have been able to perform a similar query in seconds on top of Druid; the cluster had a run rate of $25k/month at the time, while I believe we pay 40k+/month just on our BQ deal. Maybe under ideal conditions BigQuery can run such a query, but I have never seen it do such things. I have experience with Snowflake, it is great if you are in AWS, but I wouldn't choose it over BigQuery. I feel that Snowflake's strength is in their data sharing protocol, which allows us to tap into the datasets of partners without major data transfer costs. We use it for that reason only.
shaklee3 7y ago
Clickhouse has had native Kafka ingest support for over a year.

leetbulb 7y ago

1) Most people handling large data streams are already using Kafka or similar. If you aren't, Druid's has pretty wide support for event ingress, including HTTP. 2) If you're looking to provide this type of analytics in the first place, you probably do want this. Being able to execute extremely fast, granular, ad-hoc queries over highly dimensional data is very powerful. I designed a reporting frontend that really took advantage of this and I always felt guilty when people would complimented me how fast it drilled down in to 10+ dimensions. 3) There are plenty of mature libraries for the Druid API. 4) This is almost inherent with any OLAP system. Although, even with high-cardinality data, Druid performs extremely well. Either way, you should have a backing data warehouse and offline, batch jobs if you need to perform BI analytics on row-by-row / non-aggregated data. Remember, OLAP sits on top of a data warehouse. 5) Yes, but technically you just need a key that increments. 6) Not sure if this is an argument against Druid. I've found the S3 deep storage / tiering to be very efficient and powerful, especially because you can create and push segments directly to storage and not run it through an indexer. S3 is also just an object store protocol specification now. There are lots of people who run S3-compatible object stores in-house. HDFS is also natively supported and another widely used storage backend in this space. Also, there are plenty of community extensions for other object stores. 7) Again, OLAP. Data flowing in to your OLAP layer should already be denormalized and ready for indexing. Also, you can join data in to your existing indices with Hadoop, etc. Druid support joins and lookups, although I've never used them. ClickHouse and other similar systems also don't do very well with joins. Maybe we have different definitions of "complex analysis," but in my experience, you can do some pretty crazy stuff with queries including writing your own JS functions, and if you're really dedicated, you can write your own extensions. One thing that I feel like a lot of people miss is that Druid is specifically an OLAP layer designed for large scale, and I mean beyond Netflix size scale (they use Druid). Every individual component of Druid is designed to scale out independently and play nicely with your existing infrastructure. Similar to ES, you have nodes that have specific roles such as event ingress / real-time compute, query handlers, query brokers, historical data / cache / compute, etc. Then you also have a bunch of supporting architecture provided for you for (re)indexing data, coordinating the entire cluster, etc. Druid is huge, not an AIO (OLAP, OLTP, DWH, etc) analytics solution, and it takes more than one person to run a larger cluster, even though I did it for a few years.

manigandham 7y ago
Relational databases with joins and full SQL support are still unmatched in flexibility, and functionality like materialized views, aggregation pipelines (and table engines for Clickhouse) allows you to do everything that Druid does with aggregated summaries while still having all the other querying abilities. Druid has a slight edge in data scale-out and indexed seeks but modern data warehouses are adding similar tiering features, along with with field indexing, full-text search, nested data records, and even in-memory rowstores for OLTP support. They're all converging on the same feature set and eventually Druid will just become another data warehouse option, although I'd still recommend Clickhouse or MemSQL at that point.
- sanderjd 7y ago
  Question for everyone in this thread: what resources do you use to follow this stuff? Are there blogs or conferences or ... that I should follow if I'm interested in this topic?
  - manigandham 7y ago
    Distributed SQL data warehouses? Start with the papers: - C-Store (column-oriented database storage and processing): http://db.lcs.mit.edu/projects/cstore/vldb.pdf http://db.lcs.mit.edu/projects/cstore/vldb.pdf and https://sookocheff.com/post/databases/c-store/ https://sookocheff.com/post/databases/c-store/ - BigQuery (the original Dremel-based query engine): https://cloud.google.com/files/BigQueryTechnicalWP.pdf https://cloud.google.com/files/BigQueryTechnicalWP.pdf - Snowflake paper: https://www.snowflake.com/resource/sigmod-2016-paper-snowflake-elastic-data-warehouse/ https://www.snowflake.com/resource/sigmod-2016-paper-snowfla... - Andy Pavlo's CMU distributed OLAP databases lecture: https://www.youtube.com/watch?v=dPMc7FZ3Gqo&t=1s https://www.youtube.com/watch?v=dPMc7FZ3Gqo&t=1s - MemSQL architecture: https://www.youtube.com/watch?v=HoompXCG5Mk https://www.youtube.com/watch?v=HoompXCG5Mk Start there and just read more about architecture of the various products. You'll learn the basics of distributed nodes that store data in column-oriented tables partitioned by a key and how to run queries over them using various performance techniques. Then you can dive into the unique differences of the various databases.

DevKoala 7y ago

You are right about Clickhouse, but other data warehouses are not optimized for the same use case of Druid and Clickhouse, OLAP. For example, RedShift and BigQuery cannot be put behind a user facing backend. BigQuery has a default limit of 50 concurrent queries, if that's your user limit, perfect. RedShift takes seconds for queries that Druid and Clickhouse can answer in milliseconds.

manigandham 7y ago
All data warehouses are designed for OLAP, that's their purpose. It doesn't require low latency though. Redshift is an always running cluster of scale-out distributed postgres forked by AWS so it can and does return results in milliseconds, very similar to Clickhouse although still not as advanced in performance techniques. Bigquery is a completely managed model that uses far greater scale-out architecture designed for througput (petabytes in seconds) rather than latency, although it has real-time streaming, BI Engine (memory cache) and materialized views so you can get pretty close today. Snowflake is another option that runs on top of major clouds using instances to access object storage and also has low latency when your cluster is running.
- DevKoala 7y ago
  Not all data warehouses are designed for OLAP. As the performance of these systems increase, it is possible to satisfy OLAP use cases with some warehouses such a BQ, but they are still not ideal as your number of concurrent queries scale and your response time requirements remain constant. The BQ team certainly doesn't consider their product an OLAP solution, neither the RedShift team. AWS is actually working on their own OLAP solution, Timestream; that is how they pitch it, as their OLAP solution. BigQuery's petabyte aggregations in seconds is a false claim. I just launched a query to count the number of records of a 1.4 PB dataset and it has taken over a minute to just schedule the query, I have 1000+ compute units available too. I have been able to perform a similar query in seconds on top of Druid; the cluster had a run rate of $25k/month at the time, while I believe we pay 40k+/month just on our BQ deal. Maybe under ideal conditions BigQuery can run such a query, but I have never seen it do such things. I have experience with Snowflake, it is great if you are in AWS, but I wouldn't choose it over BigQuery. I feel that Snowflake's strength is in their data sharing protocol, which allows us to tap into the datasets of partners without major data transfer costs. We use it for that reason only.

shaklee3 7y ago

Clickhouse has had native Kafka ingest support for over a year.