Search Benchmarking: RediSearch vs. Elasticsearch(redislabs.com)
redislabs.com
Search Benchmarking: RediSearch vs. Elasticsearch
https://redislabs.com/blog/search-benchmarking-redisearch-vs-elasticsearch/
80 comments
Not to mention that Elasticsearch is excellent for non-text search.
One application I worked on indexes a Postgres database into Elasticsearch for live front-end queries. We index every single field, sometimes hundreds of fields in a single index. ES does this easily. Thanks to Lucene's quasi-columnar/quasi-LSM tree storage, new indexed fields aren't very expensive, and searches -- even fairly complicated ones -- are very fast.
ES is also extremely fast at aggregations. Even complex multi-level aggregations (e.g. group by date, then multiple nested buckets by different fields with "top k" results for each) take just a few hundred milliseconds for latge million-document datasets.
Where ES has problems are areas like replication, consistency and memory usage. It's very hard to tune ES; due to JVM GC and caches, it's basically impossible to predict how much RAM ES will need, and OOMs are common. There's also still no way to ask for a consistent index on query; the best you can do is use "waitfor=refresh" on indexing, which is the wrong time for it. I'd love a consistent Raft-based ES.
One application I worked on indexes a Postgres database into Elasticsearch for live front-end queries. We index every single field, sometimes hundreds of fields in a single index. ES does this easily. Thanks to Lucene's quasi-columnar/quasi-LSM tree storage, new indexed fields aren't very expensive, and searches -- even fairly complicated ones -- are very fast.
ES is also extremely fast at aggregations. Even complex multi-level aggregations (e.g. group by date, then multiple nested buckets by different fields with "top k" results for each) take just a few hundred milliseconds for latge million-document datasets.
Where ES has problems are areas like replication, consistency and memory usage. It's very hard to tune ES; due to JVM GC and caches, it's basically impossible to predict how much RAM ES will need, and OOMs are common. There's also still no way to ask for a consistent index on query; the best you can do is use "waitfor=refresh" on indexing, which is the wrong time for it. I'd love a consistent Raft-based ES.
Could you talk about the usecase here ? This is very interesting from a db query tuning perspective. What kind of queries work well in scenarios like this ?
I thought search engines are only useful in ranking based searches ...so you accept a degree of error margin wrt databases.
Any non-joining OLTP query will perform very well with ES. It is particularly effective with low-cardinality fields where in a traditional relational database you would not benefit from a B-tree index and a database like Postgres typically would revert to a sequential scan over the entire table. Column intersections in Lucene are extremely efficient, basically streaming sorted vectors of document IDs from RAM.
Where ES is not optimal is when you need joins. That said, doing left outer joins -- which is typical in web workloads where you may have something like an "articles" table that you want to query with filters and then join against "authors" and "categories" without filters to fetch connected data -- on the client side with some basic parallelization is surprisingly effective. Currently doing that in some apps where we get <100 millisecond performance even when fetching maybe 5-6 related objects per result.
Where ES is not optimal is when you need joins. That said, doing left outer joins -- which is typical in web workloads where you may have something like an "articles" table that you want to query with filters and then join against "authors" and "categories" without filters to fetch connected data -- on the client side with some basic parallelization is surprisingly effective. Currently doing that in some apps where we get <100 millisecond performance even when fetching maybe 5-6 related objects per result.
Do you do left outer join on elasticsearch...or do you do it in the client code ? I'm trying to figure out if elasticsearch supports these query types. It's something I never thought about.
In client code. ES doesn't do joins (ignoring a rather weak feature called "nested documents").
This. The best benchmarking for search engines is:
1) is the main measure users care about, and in my experience is best evaluated by building a search in each system with the same corpus and giving to subject-matter experts.
1. Does it return relevant results?
2. Can it handle complex queries?
1) is the main measure users care about, and in my experience is best evaluated by building a search in each system with the same corpus and giving to subject-matter experts.
The classic metrics here are recall and precision. Does it return all of the results that it should and does it list the best results first.
Without a good search engine you might have the results you needed plus lots of other results. You'd have to scroll to page 20 of your results to actually see the result that you wanted, which means it wasn't very precise.
Think of internet search engines pre-google. With e.g. alta vista you had great recall but extremely poor precision. You'd often be scrolling multiple pages of results. Google turned that around by having great precision and similar recall. They made it so good that they implemented the "i feel lucky" button.
The trick with search is to have great precision and still good enough recall. That's super hard because what is precise is very subjective and highly dependent on your usecases, data, languages, etc.
This is why Elasticsearch is such a hugely complicated product: it includes a lot of solutions for essentially any use case you can imagine around search.
I have no experience with Redisearch; so I'll reserve my judgment. But this article is not doing it any favors.
There are competing things out there for Elasticsearch. Most of the serious ones also use Apache Lucene (e.g. Solr). Some of the upcoming ones are attempting to rebuild what Lucene does and may or may not be good enough depending on your use case. There have been some lucene ports over the years, including a C port. Most of those have fallen behind or are no longer maintained. The Java implementation is actually pretty good as is and has had a lot of performance and optimization work done to it over the years. You'd be hard pressed to build something as good and as fast without essentially using the same algorithms and reinventing a lot of the same wheels.
IMHO the current effort to build a search engine in Rust makes a lot of sense. The language is uniquely suited to doing the kinds of things Lucene does and they seem to be pretty serious about doing things properly.
Without a good search engine you might have the results you needed plus lots of other results. You'd have to scroll to page 20 of your results to actually see the result that you wanted, which means it wasn't very precise.
Think of internet search engines pre-google. With e.g. alta vista you had great recall but extremely poor precision. You'd often be scrolling multiple pages of results. Google turned that around by having great precision and similar recall. They made it so good that they implemented the "i feel lucky" button.
The trick with search is to have great precision and still good enough recall. That's super hard because what is precise is very subjective and highly dependent on your usecases, data, languages, etc.
This is why Elasticsearch is such a hugely complicated product: it includes a lot of solutions for essentially any use case you can imagine around search.
I have no experience with Redisearch; so I'll reserve my judgment. But this article is not doing it any favors.
There are competing things out there for Elasticsearch. Most of the serious ones also use Apache Lucene (e.g. Solr). Some of the upcoming ones are attempting to rebuild what Lucene does and may or may not be good enough depending on your use case. There have been some lucene ports over the years, including a C port. Most of those have fallen behind or are no longer maintained. The Java implementation is actually pretty good as is and has had a lot of performance and optimization work done to it over the years. You'd be hard pressed to build something as good and as fast without essentially using the same algorithms and reinventing a lot of the same wheels.
IMHO the current effort to build a search engine in Rust makes a lot of sense. The language is uniquely suited to doing the kinds of things Lucene does and they seem to be pretty serious about doing things properly.
Definitely. In my mind the very first questions you should ask when evaluating search are "Do i need faceted search?" "Do I need boolean logic? proximity? stemming?". Because the answers to those questions will cut the field way down.
That's why some of these benchmarks (redis and the go search engine posted last week) seem a little apples/oranges to me.
That's why some of these benchmarks (redis and the go search engine posted last week) seem a little apples/oranges to me.
I'm not very familiar with Redis but wouldn't it shine at set-based operations like faceting? You could also pre-tokenize the input data to RediSearch with Lucene analyzers.
Not to mention spelling correction, synonyms, nested taxonomies, etc. Search is incredibly complex, and I always snort when I see someone trying to create one from scratch.
I was just going to ask: Will SQL work with spelling corrections?
I was under the impression that if you wanted to do auto-complete, you need to handle mis-spellings, and that ElasticSearch is one of the best options for this.
I was under the impression that if you wanted to do auto-complete, you need to handle mis-spellings, and that ElasticSearch is one of the best options for this.
SQL doesn't. Some relational databases have full-text search extensions like Postgres and SQL Server but they offer the basic stemming and trigram stuff, no spelling or synonyms. You can get an autocomplete working using wildcard matches but you won't be able to recognize that a word is misspelled without maintaining your own dictionary.
AFAIK, SQL engines will do a fairly reasonable full-text search, but if you need anything more, you have to upgrade to a full-featured search engine.
This X 2. I feel all these people who feel anything else is a viable alternative to Elasticsearch have a dumb, simple, small-scale use case, where even full-text search over Postgres would suffice.
>> all these people who feel anything else is a viable alternative to Elasticsearch have a dumb, simple, small-scale use case
I have a search use case. I want to create a simple language model where each token in the lexicon gets a unique ID (or ordinal) that I can use to create a more sophisticated model where each document is represented as a vector as wide as there are unique tokens and use clustering and give each cluster a unique ID (or ordinal) so that I can create an even more sophisticated language model, one with built-in semantic understanding. A natural language data structure, if you will, with multiple layers. I want to store the entire WWW in such a model. So I'm building a language model framework that is not build on Lucene because I'm not obliged to use ES in that capacity.
I feel you are wrong to call my use case simple and small scale.
I have a search use case. I want to create a simple language model where each token in the lexicon gets a unique ID (or ordinal) that I can use to create a more sophisticated model where each document is represented as a vector as wide as there are unique tokens and use clustering and give each cluster a unique ID (or ordinal) so that I can create an even more sophisticated language model, one with built-in semantic understanding. A natural language data structure, if you will, with multiple layers. I want to store the entire WWW in such a model. So I'm building a language model framework that is not build on Lucene because I'm not obliged to use ES in that capacity.
I feel you are wrong to call my use case simple and small scale.
> Where elasticsearch shines is in complex queries ...
If the "Multi-tenant indexing benchmark" is accurate it seems like it might be a robustness concern for ES. "Elasticsearch crashed after 921 indices and just couldn’t cope with this load." -- does that mean memory exhaustion or some other crash? If it's the latter, it seems like a quality problem more than a performance one.
If the "Multi-tenant indexing benchmark" is accurate it seems like it might be a robustness concern for ES. "Elasticsearch crashed after 921 indices and just couldn’t cope with this load." -- does that mean memory exhaustion or some other crash? If it's the latter, it seems like a quality problem more than a performance one.
This is exactly why Elasticsearch has a soft limit of 1000 shards per node since version 7.0: https://www.elastic.co/guide/en/elasticsearch/reference/7.0/...
This benchmark used 4605 shards (5 per index) on a single node, which is way above the recommended number.
Also, to prevent oversharding, the default number of shards per index has been changed to 1 in 7.0.
This benchmark used 4605 shards (5 per index) on a single node, which is way above the recommended number.
Also, to prevent oversharding, the default number of shards per index has been changed to 1 in 7.0.
Yeah, anyone creating 921 indexes in the same cluster hasn't read the ES docs[0]. Utilizing aliases and possibly routing is a significantly better design.
I think we can all agree that misusing a tool, after appropriate documentation has been published, shouldn't be a considered a fault of the tool.
[0] https://www.elastic.co/guide/en/elasticsearch/guide/current/...
I think we can all agree that misusing a tool, after appropriate documentation has been published, shouldn't be a considered a fault of the tool.
[0] https://www.elastic.co/guide/en/elasticsearch/guide/current/...
Very very few customers actually have 921 indices in production. That is an insane amount.. by a large factor.
Judging from what I see on irc and when I get called for “our ES cluster is on fire, can you put it out?”, 921 indices is not much. I sometimes joke that I could replace myself with a bot that answers “less indices, less shards” to each and every question about performance and that bot could solve 90% of the problems at a fraction of my cost. But alas, nobody wants to pay for a visit from my bot.
each ES shard is actually a lucene index, and it uses memory... why would anyone need thousand of indices on a single node?
What's the difference, memory-wise, between a single shard and two shards holding half the data each?
I'll give a very, very basic example of why two shards with "half" the data is less optimal. More complicated optimizations can be left as an exercise to the reader.
Lets pretend the only data structure within a Lucene Shard is a Trie.
Given 4 strings, ["Hello", "World", "Help", "Thanks"]; A total of 20 chars.
With one shard, Lucene can utilize prefixing to find overlap between "Hello" and "Help". Meanwhile, "World" and "Thanks" are always fully stored. Resulting in a Trie of only 17 chars, i.e. a whopping (1 - 17/20) = 15% storage optimization!
With two shards Lucene potentially looses that optimization.
If the split is: ["Hello", "Help"], ["World", "Thanks"] then Lucene needs to store two Tries with 6 chars and 11 chars. Totaling: 17 chars and we still get a 15% optimization.
However, if the split is: ["Hello", "World"], ["Help", "Thanks"] then Lucene needs to store two Tries with 10 chars and 10 chars. Totaling: 20 chars for a 0% optimization :(
Now lets get back to reality, and remember that Lucene not only uses a LOT of optimizations (for both storage, and query performance), but also (for many reasons) pre-processing the data to find optimal shard placement is generally not an option, and the amount of data being indexed is generally so large that these optimizations are extremely powerful.
Just to make sure this comment is never used out of context: Sharding is still extremely important, and using a single shard is only recommended if you have insignificant amounts of data.
Lets pretend the only data structure within a Lucene Shard is a Trie.
Given 4 strings, ["Hello", "World", "Help", "Thanks"]; A total of 20 chars.
With one shard, Lucene can utilize prefixing to find overlap between "Hello" and "Help". Meanwhile, "World" and "Thanks" are always fully stored. Resulting in a Trie of only 17 chars, i.e. a whopping (1 - 17/20) = 15% storage optimization!
With two shards Lucene potentially looses that optimization.
If the split is: ["Hello", "Help"], ["World", "Thanks"] then Lucene needs to store two Tries with 6 chars and 11 chars. Totaling: 17 chars and we still get a 15% optimization.
However, if the split is: ["Hello", "World"], ["Help", "Thanks"] then Lucene needs to store two Tries with 10 chars and 10 chars. Totaling: 20 chars for a 0% optimization :(
Now lets get back to reality, and remember that Lucene not only uses a LOT of optimizations (for both storage, and query performance), but also (for many reasons) pre-processing the data to find optimal shard placement is generally not an option, and the amount of data being indexed is generally so large that these optimizations are extremely powerful.
Just to make sure this comment is never used out of context: Sharding is still extremely important, and using a single shard is only recommended if you have insignificant amounts of data.
I can’t quantify in bytes, but a shard comes with quite a bit of baggage, it has a mapping and other data stores in the cluster state, some other bookkeeping data attached to it (where, is it primary or replica, in sync or not, ...) and each shard allocates a chunk of the HEAP for index operations. That chunks size depends on whether the shard received writes or not and ranges from 5MB to 256(?)MB. The exact maximum varies from version to version and I don’t think it’s in the ES docs.
In a production setting, I wouldn't recommend doing ElasticSearch multi-tenancy in this manner. Indexes aren't free.
To counter some of the comments here, and after looking at the sources, the RediSearch module is pretty capable and matches a lot of the Elasticsearch features: https://github.com/RedisLabsModules/RediSearch
Agree with the general claim that this benchmark is poor though. A real study of complex searches with faceting, ranking and ordering against both databases in a distributed setup would be much more interesting.
Agree with the general claim that this benchmark is poor though. A real study of complex searches with faceting, ranking and ordering against both databases in a distributed setup would be much more interesting.
> "Show me every match where this field contains 'extinction' within 10 words of 'impact crater' but NOT containing 'oceanic' and the publish date is > last month and one of the subjects is anthropology"
...and then aggregate into time-based buckets, and within each bucket split the results by this field, and then...
...and then aggregate into time-based buckets, and within each bucket split the results by this field, and then...
RediSearch can do all of that.
I've seen ppl use it as key value store, time series database( i think they have some apm support too) , nosql datastore.
What would be your go-to solution for a basic word search - lets say you only have a few MBs of data - not GBs...
I love plain old Redis, but I'm not thrilled with the extension modules from Redis Labs.
I experimented with RediSearch using 20 GB of Reddit posts and I was very underwhelmed.
First, 20 GB of raw data explodes into 75 GB once it's in RediSearch with zero fault tolerance. While I'd expect some expansion with inverted indexes and word frequencies by document, a 3.75 multiple seems high.
And since this is Redis, it's all in RAM, including indexes and raw documents, all uncompressed. That's not cheap. Add replicas for fault tolerance and the RAM needed for a decent sized cluster could be 10x the size of the raw data.
Then the tooling and documentation is very limited. Redis Labs provides a Python client, but it doesn't support basic features like returning the score with each document, even though RediSearch provides this capability if you query it directly.
Finally, I found stability issues with Redis when the RediSearch module is installed. Using the Python client provided by RedisLabs, certain queries would predictably crash every node in the cluster.
Redis itself is rock solid, but Redis with the RediSearch module feels fragile.
Overall, interesting concept but not ready for production use by any means.
I experimented with RediSearch using 20 GB of Reddit posts and I was very underwhelmed.
First, 20 GB of raw data explodes into 75 GB once it's in RediSearch with zero fault tolerance. While I'd expect some expansion with inverted indexes and word frequencies by document, a 3.75 multiple seems high.
And since this is Redis, it's all in RAM, including indexes and raw documents, all uncompressed. That's not cheap. Add replicas for fault tolerance and the RAM needed for a decent sized cluster could be 10x the size of the raw data.
Then the tooling and documentation is very limited. Redis Labs provides a Python client, but it doesn't support basic features like returning the score with each document, even though RediSearch provides this capability if you query it directly.
Finally, I found stability issues with Redis when the RediSearch module is installed. Using the Python client provided by RedisLabs, certain queries would predictably crash every node in the cluster.
Redis itself is rock solid, but Redis with the RediSearch module feels fragile.
Overall, interesting concept but not ready for production use by any means.
In order for me to trust a benchmark, it needs to be a lot more transparent than this
- Show the code that runs the bench mark
- Give opportunities for everyone to recreate the benchmark
- Give opportunities for every technology to 'respond' and point out where the benchmark/tech configuration is wrong (ie "PRs welcome")
Otherwise, this just looks like cherry-picked data points, and even those I won't trust. Nor would I show this to any of my clients (whom I help select search engine technology). I dearly hope nobody makes real decisions based on this blog post until the code, and everything is opened up.
- Show the code that runs the bench mark
- Give opportunities for everyone to recreate the benchmark
- Give opportunities for every technology to 'respond' and point out where the benchmark/tech configuration is wrong (ie "PRs welcome")
Otherwise, this just looks like cherry-picked data points, and even those I won't trust. Nor would I show this to any of my clients (whom I help select search engine technology). I dearly hope nobody makes real decisions based on this blog post until the code, and everything is opened up.
>Component: Search Engine
>RediSearch: Dedicated engine based on modern and optimized data-structures
>ElasticSearch: 20 years old Lucene engine
The implications made here make me actually angry.
>RediSearch: Dedicated engine based on modern and optimized data-structures
>ElasticSearch: 20 years old Lucene engine
The implications made here make me actually angry.
Lucene: over 20 years has been battle tested, optimized, and improved to the point where it’s running search almost everywhere
RedisSearch: new shiny thing built on top of Redis that is used in a couple of niche places.
I’ll take Lucene please
RedisSearch: new shiny thing built on top of Redis that is used in a couple of niche places.
I’ll take Lucene please
If 2-word queries is all you need, why would you even consider elasticsearch? This benchmark is pure marketing IMHO.
RedisLabs seems to really be abusing Redis's popularity on HN.
I've seen a lot of posts like this easily make it to the front page only because a lot of HN-ers are Redis fanboys (rightfully so: Redis is great). But then you read the post and it _appears_ to be marketing garbage.
I've seen a lot of posts like this easily make it to the front page only because a lot of HN-ers are Redis fanboys (rightfully so: Redis is great). But then you read the post and it _appears_ to be marketing garbage.
From the license fiasco to this. RedisLabs tries hard to be the new Microsoft.
The article is a mess of misspellings and misquotes. Also why two distributed search engine were tested on a single node? That's a a meaningless test.
"Elasticsearch crashed after 921 indices" ... Shards: "20 for the multi-tenant benchmark".. 921 * 20 = 18420. Shards have state; they have overhead. Why wouldn't they pick one shard for that benchmark? It's either intentional misconfiguration, or poor understanding of sharding.
From the article: "Here, we simulated a multi-tenant e-commerce application where each tenant represented a product category and maintained its own index. For this benchmark, we built 50K indices (or products), which each stored up to 500 documents (or items), for a total of 25 million indices. RediSearch built the indices in just 201 seconds, while running an average of 125K indices/sec. However, Elasticsearch crashed after 921 indices and clearly was not designed to cope with this load."
No sane elasticsearch engineer would make a new index for each product. They would just have a single index with a product_id field for each sub-item. If you needed product level information, you would create a second index for that. You'd use two indexes not O(#Product) indexes.
They just created a botched benchmark by using ES incorrectly. It's like driving a car backwards and then complaining it has poor max speed. ES could easily handle this type of problem if done correctly.
No sane elasticsearch engineer would make a new index for each product. They would just have a single index with a product_id field for each sub-item. If you needed product level information, you would create a second index for that. You'd use two indexes not O(#Product) indexes.
They just created a botched benchmark by using ES incorrectly. It's like driving a car backwards and then complaining it has poor max speed. ES could easily handle this type of problem if done correctly.
"The more advanced multi-tenant use case – where RediSearch was able to complete 25 million indices in just 201 seconds or ~125K indices/sec, while Elasticsearch crashed after it indexed 921 documents, showing that it was not designed to cope with this level of load." previously stated that "Elasticsearch crashed after 921 indices and just couldn’t cope with this load."
It's hard to mistake documents for indices. Both original and the currently edited statement sound strongly suspect and make me question the benchmarking methodology used. What caused the ES to crash after indexing 921 documents? Why is comparing indexing speeds on a 1-node setup even a legit benchmarking test?
It's hard to mistake documents for indices. Both original and the currently edited statement sound strongly suspect and make me question the benchmarking methodology used. What caused the ES to crash after indexing 921 documents? Why is comparing indexing speeds on a 1-node setup even a legit benchmarking test?
I fail to see how the creation of 50K indices on elasticsearch is a meaningful benchmark, that's just not how it's supposed to be used.
Also as others said, testing a distributed system on a single node makes little sense... as it is a benchmark which is not reproducible as we don't know how the data was queried and indexed
This benchmark is pretty misleading. And not the mention that elasticsearch is free for multi node deployments while redis search is not.
The intent is nice, but the weird clippy-style avatar in the bottom right is kinda annoying. I'm just trying to read the article not engage in a conversation.
> Dataset source: wikidump Date: Feb 7, 2019 docs: 5.6M size: 5.3 GB
"wikidump" links to https://dumps.wikimedia.org/enwiki/latest/ , which has thousands of files, none of which are 5GB and make sense. That's a very poor corpus link!
It says "Feb 7, 2019", so it probably means https://dumps.wikimedia.org/enwiki/20190120/ or https://dumps.wikimedia.org/enwiki/20190201/ ... maybe. They don't have any obvious 5.3GB files.
"wikidump" links to https://dumps.wikimedia.org/enwiki/latest/ , which has thousands of files, none of which are 5GB and make sense. That's a very poor corpus link!
It says "Feb 7, 2019", so it probably means https://dumps.wikimedia.org/enwiki/20190120/ or https://dumps.wikimedia.org/enwiki/20190201/ ... maybe. They don't have any obvious 5.3GB files.
If anyone is looking for real benchmarks of ES, check out this page and leave the BS benchmarks aside :-)
https://elasticsearch-benchmarks.elastic.co/
Someone needs to edit this article. There are misspellings and typos all over the place.
It's Friday, the task was to deliver the aritcle by the weekend :)
I'm curious if this scales down well. The test was done on "One AWS c4.8xlarge with 36vCPU and 60GiB Memory". But could I run this on a tiny vps to index, search, catalog my million-odd documents?
SQLite's FTS5 module works surprisingly well on that number of documents, and stores the search index on disk instead of RAM.
You can check out Sonic[1] and Tantivy[2] both are lightweight search engine
[1] https://github.com/valeriansaliou/sonic
[2] https://github.com/tantivy-search/tantivy
[1] https://github.com/valeriansaliou/sonic
[2] https://github.com/tantivy-search/tantivy
I'd guess redis performs _better_ in that case since there minimum overhead for redis is much lower than elasticsearch.
A problem with RediSearch, at least for me is:
Note: clustering is only available in RediSearch’s Enterprise version
https://redislabs.com/redis-enterprise/technology/redis-sear...
At least with ES i can build and play with the clustering of the nodes. This is probably why they only made a 1 node ES, because they would have to push their Enterprise software to do make a cluster of RediSearch. Maybe i am wrong.
Note: clustering is only available in RediSearch’s Enterprise version
https://redislabs.com/redis-enterprise/technology/redis-sear...
At least with ES i can build and play with the clustering of the nodes. This is probably why they only made a 1 node ES, because they would have to push their Enterprise software to do make a cluster of RediSearch. Maybe i am wrong.
RedisLabs has done great work in developing Redis but these extensions to retrofit Redis into a multi-model database have issues.
Raw latency is usually not the primary concern most of the time and having everything in RAM can be a major cost problem, further compounded by the lack of compression available as with other persistent stores. The RESP protocol is also overloaded and hard to work with when dealing with json and search queries.
Raw latency is usually not the primary concern most of the time and having everything in RAM can be a major cost problem, further compounded by the lack of compression available as with other persistent stores. The RESP protocol is also overloaded and hard to work with when dealing with json and search queries.
Does not tell us the settings for text analysis done by two engines. Secondly on query side - again, scoring settings of RediSearch vs Elastic are not discussed.
With that it’s just 2 points in space which gives us little information to deduce 58% faster at X or whatever.
With that it’s just 2 points in space which gives us little information to deduce 58% faster at X or whatever.
In the fine print: number of shards for the multi-tenant benchmark for Redisearch was increased from 5 to 20; but kept the same (5) for Elastisearch.
This is why the only reliable benchmark is the one you do on your data.
This is why the only reliable benchmark is the one you do on your data.
Anyone can make a search engine fast. It's much harder to make it good.
So according to HN, they’ve proved RediSearch is actually inferior
No, they just haven't proven anything.
Is the RediSearch's aggregation comparable with ES's? Speed has lower priority when there are missing features.
PS: Crashes are never good though...
PS: Crashes are never good though...
WOW. Hahahaha.
This is a massive misconfiguration of an elastic search cluster. 50k indices? 500 documents per index?
500 records per index at 5shards/index is 100 records per shard.
Yeah, let's shard our data so much that we introduce tremendous amounts of disk i/o overhead!!!
Author should learn how to properly configure an ES cluster before posting ridiculous benchmarks like this.
What an utter pile of garbage benchmark this is.
This is a massive misconfiguration of an elastic search cluster. 50k indices? 500 documents per index?
500 records per index at 5shards/index is 100 records per shard.
Yeah, let's shard our data so much that we introduce tremendous amounts of disk i/o overhead!!!
Author should learn how to properly configure an ES cluster before posting ridiculous benchmarks like this.
What an utter pile of garbage benchmark this is.
Oh fuck me, I didn't even realize they used a single instance (node).
To expand a little bit, the whole point of using multiple shards per index in an ES cluster is so that the shards spread across multiple nodes (servers) and distribute the load (disk i/o) and handle redundancy. ES automatically scales and reshuffles its shards across multiple nodes in the cluster to handle fault-tolerance as well. If one or more nodes go down, the cluster still has all of the data through replica shards etc...
Either way, in this particular case, the data is so small, having 5 shards per index with 50k indices results in 250k shards for 5GBs of data.
5GB / 250k shards = 20kb per shard.
You have shards of size ~ 20kb ... total cluster misconfiguration.
To expand a little bit, the whole point of using multiple shards per index in an ES cluster is so that the shards spread across multiple nodes (servers) and distribute the load (disk i/o) and handle redundancy. ES automatically scales and reshuffles its shards across multiple nodes in the cluster to handle fault-tolerance as well. If one or more nodes go down, the cluster still has all of the data through replica shards etc...
Either way, in this particular case, the data is so small, having 5 shards per index with 50k indices results in 250k shards for 5GBs of data.
5GB / 250k shards = 20kb per shard.
You have shards of size ~ 20kb ... total cluster misconfiguration.
Isn't that exactly what they're trying to demonstrate though? That all this arcana you have to invoke to get a stable ES cluster barely breaks a sweat on Redisearch?
The specific test deployment was multitenant anyway-- you can't account or optimize for what tenants are going to index.
The specific test deployment was multitenant anyway-- you can't account or optimize for what tenants are going to index.
I'm not familiar with RediSearch, but I'm just trying to point out that you can't misconfigure ES and then benchmark against a misconfigured cluster. This is comparing apples to oranges. Not to mention I'm not sure of the feature difference between the 2 search engines, but I'd bet ES is much more feature rich, thus its use cases are vastly different. If you are just comparing text search, sure, maybe redis is faster. But at that point, so is a simple sql database, when compared to a misconfigured ES cluster.
I'm not familiar with Redisearch either but I agree, there's definitely a bent to this in that they made something that isn't ES, then compare it to ES via a a benchmark that shows how poorly ES performs at...being something other than ES.
The impression I got was that they were trying to demonstrate for two specific workloads, how much more a single node of RS can do than a single node of ES, and that we should extrapolate the savings and performance if scaled out from there.
A properly deployed ES cluster versus a single RS node isn't a fair comparison either. It's a strained comparison in any case.
The impression I got was that they were trying to demonstrate for two specific workloads, how much more a single node of RS can do than a single node of ES, and that we should extrapolate the savings and performance if scaled out from there.
A properly deployed ES cluster versus a single RS node isn't a fair comparison either. It's a strained comparison in any case.
"The specific test deployment was multitenant anyway-- you can't account or optimize for what tenants are going to index."
So in other words:
"If your specific use case is supporting 50,000 customers each having around 500 documents and only needing basic text search queries and relevance is not a major concern, RedisLabs Search might give you better performance than ElasticSearch!"
(This is assuming there isn't a different way to configure ElasticSearch to work for this scenario, that gives similar performance.)
So in other words:
"If your specific use case is supporting 50,000 customers each having around 500 documents and only needing basic text search queries and relevance is not a major concern, RedisLabs Search might give you better performance than ElasticSearch!"
(This is assuming there isn't a different way to configure ElasticSearch to work for this scenario, that gives similar performance.)
I wouldn't call that "arcana", that's just ES and lucene basics
this is how comparison benchmarks are done when you need to reach certain results. I've even had it done to me at my job!
When you point out the flawed methodology you come across like a luddite or sour grapes or whatever else.
When you point out the flawed methodology you come across like a luddite or sour grapes or whatever else.
You just say "artificial tests produce artificial results, bye".
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How silly to emphasize things like "built as a C extension" and "uses modern data-structures" as if these were useful criteria for choosing a search engine.
It's about minimizing the effort needed to find what you're looking for. Speed of index construction time, unless we're talking orders of magnitude, isn't really meaningful. I don't know if this is just a really clumsy attempt at "marketing" or what, but I can't imagine this is going to convince anyone to drop es for this thing.
It's about minimizing the effort needed to find what you're looking for. Speed of index construction time, unless we're talking orders of magnitude, isn't really meaningful. I don't know if this is just a really clumsy attempt at "marketing" or what, but I can't imagine this is going to convince anyone to drop es for this thing.
Also complaining that "Lucene is 20 years old" is about the same as saying "Linux is ~30 years old"
Lucene is a pretty rock-solid open source project that has been battle tested over those 20 years and had some of the best engineers in the world improve over a long time frame. That's an asset for Lucene!
Lucene is a pretty rock-solid open source project that has been battle tested over those 20 years and had some of the best engineers in the world improve over a long time frame. That's an asset for Lucene!
That's a testament to how hard search engines are. Armies of engineers spent years getting to this point. This project just started as a POC for Redis modules, but this is really forced and futile. Trying to do this is like attaching wings to a motorcycle - it's not how search works. This falls apart beyond a primitive word lookup, which you can do with any SQL database. And it has been done, MANY times.
> Lucene is 20 years old
Damn I feel old. I remember when Lucene was hot new kid in the block.
Damn I feel old. I remember when Lucene was hot new kid in the block.
If you only need very basic word search, ES is probably not worth the complexity in your stack, especially if you're already running a SQL database with decent plaintext search.
Where elasticsearch shines is in complex queries: "Show me every match where this field contains 'extinction' within 10 words of 'impact crater' but NOT containing 'oceanic' and the publish date is > last month and one of the subjects is anthropology"