I am picking one of the 2 search engines above for a project, and so far both of them have shown to be similar in functionalities.
At least for the requirements that I have:
Proximity Search
Boolean queries
query over all fields
Boolean queries
Retrieval of original indexed document
Real time search requirements, as soon as I index a document, it should be available
Besides that I should have around 1 single type of document, in about 40 million documents - roughly 2 TB of data
that's basically what I need, my questions would be:
Does one search engine perform better than the other considering my dataset? Such as offering better indexing rates or Search Rates?
Am I loosing anything by going with Solr(considering my requirements)?
Solr is my choice at the moment.
some thoughts:
nobody can tell you about which one would perform best for you unless you benchmark in your realistic conditions
for %99 of users, any of the two would work perfectly
if you want to go with one of them (for any reason: you like it, your devs want to try it, you like the logo, whatever), then, don't sweat it, both are very capable.
Related
I am not experienced in ES (my background is more of relational databases) and I am trying to achieve the goal of having a search bar in my web application to search the entire content of it (or the content I will be willing to index in ES).
The architecture implemented is Jamstack with a gatsby application fetching content (sometimes at build time, sometimes at runtime) from a strapi application (headless cms). In the middle, I developed a microservice to write the documents created in the strapi application to the ES database. At this moment, there is only one index for all the documents, regardless the type.
My problem is, as the application grows and different types of documents are created (sometimes very different from one another, as example I can have an article (news) and a hospital) I am having hard time to correctly query the database as I have to define a lot of specific conditions when making the query (to cover all types of documents).
My solution to this is to keep only one index and break down the query in several ones and when the user hits the search button those queries are run and the results will be joined together before being presented OR break down the only index into several ones, one per document which leads me to another doubt, is it possible to query multiple indexes at once and define specific index fields in the query?
Which is the best approach? I hope I could make my self clear in this.
Thanks in advance.
According to the example you provided, where one type of document can be of type news and another type is hospital, it makes sense to create multiple indices(but you also need to tell, how many such different types you have). there are pros and cons with both the approach and once you know them, you can choose one based on your use-case.
Before I start listing out the pros/cons, the answer to your other question is that you can query multiple indices in a single search query using multi-search API.
Pros of having a single index
less management overhead of multiple indices(this is why I asked how many such indices you may have in your application).
More performant search queries as data are present in a single place.
Cons
You are indexing different types of documents, so you will have to include a complex filter to get the data that you need.
Relevance will not be good, as you have a mix of documents which impacts the IDF of similarity algo(BM25), and impacts the relevance.
Pros of having a different index
It's better to separate the data based on their properties, for better relevant results.
Your search queries will not be complex.
If you have really huge data, it makes sense to break the data, to have the optimal shard size and better performance.
cons
More management overhead.
if you need to search in all indices, you have to implement multi-search and wait for all indices search result, which might be costly.
I'm planning to use Elasticsearch for a social network kind of platform where users can post "updates", be friends with other users and follow their friends' feed. The basic and probably most frequent query will be "get posts shared with me by friends I follow". This query could be augmented by additional constraints (like tags or geosearch).
I've learned that social networks usually take a fan-out-on-write approach to disseminate "updates" to followers so queries are more localized. So I can see 2 potential indexing strategies:
Store all posts in a single index and search for posts (1) shared with the requester and (2) whose author is among the list of users followed by the requester (the "naive" approach).
Create one index per user, inject posts that are created by followed users and directly search among this index (the "fan-out" approach).
The second option is obviously much more efficient from a search perspective, although it presents sync challenges (like the need to delete posts when I stop following a friend, for example). But the thing I would be most concerned with is the multiplication of indices; in a (successful) social network, we can expect at least tens of thousands of users...
So my questions here are:
how does ES cope with a very high number of indices? can it incur performance issues?
any thoughts about a better indexing strategy for my particular use-case?
Thanks
Each elasticsearch index shard is a separate Lucene index, which means several open file descriptors and memory overhead. Generally, even after reducing number of shards per index from default 5, the resource consumption in index-per-user scenario may be too large.
It is hard to give any concrete numbers, but my guess is that if you stick to two shards per index, you would be able to handle no more than 3000 users per m3.medium machine, which is prohibitive in my opinion.
However, you don't necessarily need to have dedicated index for every user. You can use filtered aliases to use one index for multiple users. From application point of view, it would look like a per-user scenario, without incurring overhead mentioned above. See this video for details.
With that being said, I don't think elasticsearch is particularly good fit for fan-out-on-write strategy. It is, however, very good solution to employ in fan-out-on-read scenario (something similar to what you've outlined as (1)):
The biggest advantage of using elasticsearch is that you are able to perform relevance scoring, typically based on some temporal features, like browsing context. Using elasticsearch to just retrieve documents sorted by timestamp means that you don't utilize its potential. Meanwhile, solutions like Redis will give you far superior read performance for such task.
Fan-out-on-write scenario means a lot of writes on each update (especially, if you have users with many followers). Elasticsearch is not a database and is not optimized for such usage-pattern. It is, however, prepared for frequent reads.
Fan-out-on-write also means that you are producing a lot of 'extra' data by duplicating info about posts. To keep this data in RAM, you need to store only metadata, like id of document in separate document storage and tags. Again, there are other formats than JSON to store and search this kind of structured data effectively.
Choosing between the two scenarios is a question about your requirements, like average number of followers, number of 'hubs' that nearly everybody follows, whether the feed is naturally ordered (e.g. by time) etc. I think that deciding whether to use elasticsearch needs to be a consequence of this analysis.
I understand the basics of search engine ranking, including the ideas of "reverse index", "vector space model", "cosine similarity", "PageRank", etc.
However, when a user submits a popular query term, it is very likely that millions of pages containing this term. As a result, a search engine still needs to sort these millions of pages in real time. For example, I just tried searching "Barack Obama" in Google. It shows "About 937,000,000 results (0.49 seconds)". Ranking over 900M items within 0.5 seconds? That really blows my mind!
How does a search engine sort such a large number of items within 1 second? Can anyone give me some intuitive ideas or point out references?
Thanks!
UPDATE:
Most of the responses (including some older discussions) so far seem to contribute the credit to "reverse index". However, as far as I know, reverse index only helps find the "relevant pages". In other words, by inverse index Google could obtain the 900M pages containing "Barack Obama" (out of over several billions of pages). However, it is still not clear how to "rank" these millions of "relevant pages" based on the threads I read so far.
MapReduce framework is unlikely to be the key component for real-time ranking. MapReduce is designed for batch tasks. When submitting a job to a MapReduce framework, the response time is usually at least a minute, which is apparently too slow to meet our request.
The question would be really relevant if we were sure that the ranking was complete. It is quite possible that the ordering provided is approximate.
Given the fluidity of the ranking results, no answer that looks reasonable could be considered incorrect. For example, if an entire section of the web were excluded from the top results, you would not notice, provided they were included later.
This gives the developers a degree of latitude entirely unavailable in almost all other domains.
The real question to ask is - how precisely do the results match the actual rank assigned to each page?
There are two major factors that influence the time it takes for you to get a response from your search engine.
The first is if you're storing your index on hard disk. If you're using a database, it's very likely that you're using the hard disk at least a little. From a cold boot, your queries will be slow until the data necessary for those queries has been pulled into the database cache.
The other is having a cache for your popular queries. It takes a lot longer to search for a query than it does to return results from a cache. Now, the random access time for a disk is too slow, so they need to have it stored in RAM.
To solve both of these problems, Google uses memcached. It's an application that caches the output of the Google search engine and feeds slightly old results to users. This is fine because most of the time the web doesn't change fast enough for it to be a problem, and because of the significant overlap in searches. You can be almost guaranteed that Barack Obama has been searched for recently.
Another issue that effects search engine latency is the network overheads.
Google have been using a custom variant of the Linux (IIRC) that has been optimised for use as a web server. They've managed to reduce some of the time it takes to start turning around results to a query.
The moment a query hits their servers, the server immediately responds back to the user with the header for the HTTP response, even before Google has finished processing the query terms.
I'm sure they have a bunch of other tricks up their sleeves, too.
EDIT:
They also keep their inverted lists sorted already, from the indexing process (it's better to process once than for each query).
With these pre-sorted lists, the most expensive operation is list intersection. Although I'm fairly sure Google doesn't rely on a vector space model, so list intersection isn't so much a factor for them.
The models that pay off the best according to the literature are the probabilistic models. As an example, you may wish to look up Okapi BM25. It does fairly well in practice within my area of research (XML Retrieval). When working with probabilistic models, it tends to be much more efficient to process document at a time instead of term at a time. What this means is that instead of getting a list of all of the documents that contain a term, we look at each document and rank it based on the terms it contains from our query (skipping documents that have no terms).
But if we want to be smart, we can approach the problem in a different way (but only when it appears to be better). If there's a query term that is extremely rare, we can rank with that first, because it has the highest impact. Then we rank with the next best term, and we continue until we've determined if it's likely that this document will be within our top k results.
One possible strategy is just rank the top-k instead of the entire list.
For example, to find the top 100 results from 1 millions hits, by selection algorithm the time complexity is O(n log k). Since k = 100 and n = 1,000,000, in practice we could ignore log(k).
Now, you only need O(n) to obtain the top 100 results out of 1 million hits.
Also I guess the use of NoSQL databases instead of RDBMS helps.
NoSQL databases scales horizontally better, and don't generate bottlenecks. Big guys like Google Facebook or Twitter use them.
As other comments/answers suggested the data might be already sorted, and they are returning offsets of the data found instead of the whole batch.
The real question is not how they sort that many results that quickly, but how do they do it when tens or hundreds of millions of people around the world are querying google at the same time xD
As Xiao said, just rank the top-k instead of the entire list.
Google tells you there are 937,000,000 results, but it won't show them all to you. If you keep scrolling page after page, after a while it will truncate the results :)
Here you go, i looked it up for you and this is what i found! http://computer.howstuffworks.com/internet/basics/search-engine.htm
This ia my theory...Its highly impossible that you are the first guy to search for a keyword.So for every keyword (or a combination) searched on a search engine, it maintains a hash of links to relevent web pages. Everytime you click a link in search results it gets a vote-up on the hashset of that keyword combination. Unfortunatly if you are the first guy, it saves your search keyword(for suggesting future searches) and starts the hashing of that keyword. So you end up with a fewer or no results at all.
The page ranking as you might be knowing depends on many other factors too like backlinks,no. Of pages refering a keyword in seaech. etc.
Regarding your update:
MapReduce framework is unlikely to be the key component for real-time ranking. MapReduce is designed for batch tasks. When submitting a job to a MapReduce framework, the response time is usually at least a minute, which is apparently too slow to meet our request.
MapReduce is not just designed for batch tasks. There are quite a lot MapReduce frameworks supporting real time computing: Apache Spark, Storm, Infinispan Distributed Executor, Hazelcast Distributed Executor Service.
Back to your question MapReduce is the key to distribute the query task to multiple nodes, and then merge the result together.
There's no way you expect to get an accurate answer to this question here ;) Anyway, here's a couple of things to consider - Google uses a unique infrastructure, in every part of it. We cannot even guess the order of complexity of their network equipment or their database storage. That is all I know about the hardware component of this problem.
Now, for the software implementation - like the name says the PageRank is a rank by itself. It doesn't rank the pages when you enter the search query. I assume it ranks it on a totally independent part of the infrastructure every hour. And we already know that Google crawler bots are roaming the Web 24/7 so I assume that new pages are added into an "unsorted" hash map and then they are ranked on the next run of the algorithm.
Next, when you type your query, thousands of CPUs independently scan thousands of different parts of the PageRank database with a gapping factor. For example if the gapping factor is 10, one machine queries the part of the database that has PageRank values from 0-9.99, the other one queries the database from 10-19.99 etc. Since resources aren't an obstacle for Google they can set the gapping factor so low (for example 1) in order for each machine to query less than 100k pages which isn't to much for their hardware. Then when they need to compile the results of your query, since they know which machine ranks exactly which part of the database they can use the 'fill the pool' principle. Let n be the number of links on each Google page. The algorithm that combines the pages returned from queries ran on all those machines against all the different parts of database needs to only fill the first n results. So they take the results from the machine querying against the highest rank of the database. If it is greater than n they're done, if not they move to the next machine. This takes only O(q*g/r) where s is the quantity of the pages Google serves, g is the gapping factor and r is the highest value of PageRank. This assumption is encouraged by the fact that when you turn to second page your query is ran once again (notice the different time taken to generate it) .
This is just my two cents, but I think I'm pretty accurate with this hypothesis.
EDIT: You might want to check this out for complexity of high-order queries.
I don't know what Google really does, but surely they use approximation. For example if the search query is 'Search engine' then the number of results will be = (no. of documents where there is one or more occurrence of the word 'search' + no. of documents where there is one or more occurrence of the word 'engine' ). This can be done in O(1) time complexity. For details read the basic structure of Google http://infolab.stanford.edu/~backrub/google.html.
I have 4 text columns of interest.
Each column is up to about 100 characters.
The text in 3 of the columns is mostly Latin words. (The data is a biological catalog, and these are names of things.)
The data is currently about 500 rows. I don't expect this to grow beyond 1000.
A small number of users (under 10) will have editing privileges to add, update, and delete data. I do not expect these users to put a heavy load on the database.
So all this suggests a pretty small data set to consider.
I need to perform a search on all 4 columns for rows where at least 1 column contains the search text (case insensitive). The query will be issued (and the results served) via a web application. I'm a bit lost about how to approach it.
PostgreSQL offers a few options for improving text searching speed. The possible options built into PostgreSQL I've been considering are
Don't try to index this at all. Just use ILIKE, LIKE on lower, or similar. (Without an index?)
Index with pg_trgm to improve search speed. I would assume that I would need to index the concatenation somehow.
Full text searching. I assume this would involve concatenating for the index also.
Unfortunately, I'm not really familiar with the expected performance of any of these or the benefits and trades off, so it's hard to know what things I should try first and what things I shouldn't even consider. Some things I have read suggest that doing the indexing for 2 and 3 is pretty slow, which conflicts with the fact that I'll be having occasional modifications going on. And the mixed language makes full text search seem unattractive since it appears to be language based, unless it can handle multiple languages simultaneously. Would I expect that for data this small, a simple ILIKE or maybe a LIKE on lower is probably fast enough? Or maybe the indexing is fast enough for the low load of modifications on data this small? Would I be better off looking for something outside the database?
Granted, I would have to actually benchmark all these to really know for sure what's fastest, but unfortunately, I don't have much time for this project. So what are the benefits and trade offs of these methods? What of these options are not appropriate for solving this type of problem? What are some other types of solutions (including potentially outside the database) worth considering?
(I suppose I might find some kind of beginner's tutorial on text searching in PG useful, but my searches turn up Full Text Search for the most part, which I don't even know if it's useful for me.)
I'm on PG 9.2.4, so any goodies pre-9.3 are an option.
Update: I've expanded this answer into a detailed blog post.
Rather than focusing purely on speed, please consider search semantics first. Define your requirements.
For example, do users need to be able to differentiate based on the order of terms? Should
radiata pinus
find:
pinus radiata
? Does the same rule apply to words within a column as between columns?
Are spaces always word separators, or are spaces within a column part of the search term?
Do you need wildcards? If so, do you need only left-anchored wildcards (think staph%) or do you need right-anchored or infix wildcards too (%ccus, p%s)? Only pg_tgrm will help you with infix wildcards. Suffix wildcards can be handled by an index on the reverse() of a word, but that gets clumsy quickly so in practice pg_tgrm is the best option there.
If you're mostly searching for discrete words and word-order isn't important, Pg's full-text search with to_tsvector and to_tsquery will be desirable. It supports left-anchored wildcard searches, weighting, categories, etc.
If you're mostly doing prefix searches of discrete columns then simple LIKE queries on a regular b-tree index per column will be the way to go.
So. Figure out what you need, then how to do it. Your current uncertainty probably stems partly from not really knowing quite what you want.
For a 1000 rows, I would guess that LIKE together with lower() should be fast enough. After a couple of queries the table will most probably be completely cached.
Regarding the indexing using pg_trgm: you are talking about "occasional" updates/inserts to the table. I would think that the additional costs of using a trigram index would only show up when you update/insert that table a lot - like several times a second.
If "occasional" only means several times an hour (or even less), then I doubt you'd see the difference in real live. I think somewhere in Depesz's blob there was also an article that compared the insert speed with and without a trigram index, but I can't find it anymore.
I am hosting a mongodb database for a service that supports full text searching on a collection with 6.8 million records.
Its text index includes ten fields with varying weights.
Most searches take less than a second. Some searches take two to three seconds. However, some searches take 15 - 60 seconds! The 15-60 second search cases are unacceptable for my application. I need to find a way to speed those up.
Searching takes 15-60 seconds when words that are very common in the index are used in the search query.
I seems that the text search feature does not support lazy parameters. My first thought was to cache a list of the 50 most common words in my text index and then ask mongodb to evaluate those last (lazy) and on top of the filtered results returned by the less common parameters. Hopefully people are still with me. For example, say I have a query "products chocolate", where products is common and chocolate is uncommon. I would like to be able to ask mongodb to evaluate "chocolate" first, and then filter those results with the "products" term. Does anyone know of a way to achieve this?
I can achieve the above scenario by omitting the most common words (i.e. "products") from the db query and then reapplying the common term filter on the application side after it has received records found by db. It is preferable for all query logic to happen on the database, but am open to application side processing for a speed payout.
There are still some holes in this design. If a user only searches common terms, I have no choice but to hit the database with all the terms. From preliminary reading, I gather that it is not recommended (or not supported) to have multiple text indexes (with different names) on the same collection. My plan is to create two identical tables, each with my 6.8M records, with different indexes - one for common words and one for uncommon words. This feels kludgy and clunky, but am willing to do this for a speed increase.
Does anyone have any insight and/or advice on how to speed up this system. I'd like as much processing to happen on the database as possible to keep it fast. I'm sure my little 6.8M record table is not the largest that mongodb has seen. Thanks!
Well I worked around these performance issues by allowing MongoDB full text search to search in OR based format. I'm prioritizing my results by fine tuning the weights on my indexed fields and just ordering by rank. I do get more results than desired, but that's not a huge problem because my weighted results that appear at the top will most likely be consumed before my user gets to less relevant results at the bottom.
If anyone is struggling with MongoDB text search performance using AND searching only, just switch back to OR and control your results using weights. It performs leaps better.
hth
This is the exact same issue as $all versus $in. $all only uses the index for the first keyword in the array. I believe your seeing the same issue here, reason why the OR a.k.a. IN works for you.