When did this place become such a low effort brain dump? I can't believe I'm so annoyed by this question. Google. Make some educated trade-off decisions based on your context.
Use HN to poll for opinions and experiences from others, not for things that take 30 minutes to resolve.
I tend to agree with you, however google isn’t as straightforward as it used to be. Every developer and their mom has a blog these days, or a brand, whatever. Sometimes just cross referencing fundamentals and bonafides is a time sink.
That said, this question probably works better somewhere like Reddit programming or some IIRC where Elasticsearch hangs out. After going that route, it’s probably fine to poll here based on that research.
As much OP did lazy work by posting this question, I don’t think your response was fair or helpful in any way. In the end you didn’t add anything to the conversation either.
What can you add to this discussion? 0 context is provided. It's not even a properly formed question. As someone who's familiar with Lucene, Solr and ElasticSearch even if I wanted to help I can't.
Sometimes posts are shit, and it's OK to call it out to hopefully improve this site collectively.
You should write one from scratch to get a deeper understanding of how hard it is to return highly relevant results quickly. Tokenizing, stemming, bag of words, and tf-idf for ranking get you to an MVP, but then you realize how good production grade search engines are today.
Solr is good. I've been wanting to try Lunr [1] for small sites.
I worked in a company previously where Solr was used to scale the business, and was not performant for us after a while.
We wrote our own search engine at that point. You are right that there are a lot of little “devil in the details” issues. But overall it was a fun experience.
This was needed to support some specific machine learning workflows in the search ranking process — which could not be used if we paid the high latency cost to first get preliminary results in Solr.
So we took a “create your own index data structures” approach with index data (both the normalized bag of words vectors and companion data like boolean filters), which allowed us to highly optimize the initial broad ranking query. Latency was low enough that it allowed the time cost of calling follow-on machine learning services.
This was for a fairly high-traffic product search engine at an online retailer. It ended up working very well and over a span of about two years we eventually rolled all search traffic onto the in-house platform, even the parts not needing the machine learning services, and our query latencies went down across all our traffic, and we retired the original Solr implementation.
Wouldn’t be the right choice for everyone, but it informs my opinion a lot about the worthwhileness of creating an in-house search engine to specifically replace Solr. I’d suspect a lot of medium-sized or large companies running Solr should seriously consider it.
I realize my comment was a little vague around Solr — my experience is specifically more with Lucene than Solr itself, and I just think of Solr as an easier entry point to Lucene. Did you consider or end building your custom search engine on top of Lucene or something else? What about using a service like Algolia if something like that were available at the time?
Really it’s not fancy or anything. We used Eigen to represent our normalized bag of words matrix (term-document matrix) as a sparse matrix in CSC and CSR format (which means the data resides in three underlying arrays for the nonzero entries, with indexing conventions for how to use them).
Boolean & multi-choice indices are just companion arrays where position i corresponds to a property of document i in the index: boolean for binary attributes (for example, whether the item has free shipping or not), or using a bigger integer space to encode more options, like say an int8 coupled with helper functions that check which bit is set, maybe for some set of 8 categories the items can be filtered by).
The “index” is just the serialized arrays backing the sparse matrix, the arrays backing the filters, and helper functions for decoding what the filter bits mean.
A query is then just applying the filters followed by performing the sparse matrix inner product and sorting.
It’s very basic, but allows you to heavily optimize it, whether optimizing for deletes, writes, certain heavily used filters, etc.
And you can of course add whatever fancy NLP stuff on top of or in place of the sparse matrix as well.
It sounds like a great approach to enable both speed in searching and customizability in indexing -- no way you get to bit level box ticking with conventional means. Thanks for explaining. Bag of Words, last I recall reading, is a statistical method for predicting the next word, so I'm curious how that played out for you.
Bag of words is an encoding method for converting some sequence of words into a long, sparse vector of word counts. The vector has one component for each possible word of the vocabulary, and if the count of word i is N, then you store N in conponent i of the bag of words vector.
If you think of these as sparse row vectors (the columns correspond to all vocabulary entries), then you store them as a matrix where you stack on another row for each “document” in your data set.
Later on when you get a new “document” at query time, you transform it into the same bag of words vector format, and then an inner product between the matrix and the query vector corresponds to a type of relevance / similarity useful for sorting into a ranked order of results.
In practical situations you have to work harder, because you need more units of text than just words (such as n-grams), and the raw term counts usually need to be weighted (e.g. matching a 3-gram probably means more than matching a single word) or normalized (e.g. longer documents happen to have more words, but that doesn’t mean they are more similar), and you need to account for results that are historically more popular or results that are newer.
It’s a very simple approach to document search, but it works well and there are extensions that utilize word embeddings or models that predict rankings of results.
Once you get a system running with the term-document matrix, it is a nice platform for more advanced experimentation and machine learning feature development.
With that little information from the OP the answer is probably: Use ES or if it's a small side project use your DB's included full text search if it's good enough.
You can go far using purely Postgres full text search. I ported a site from Solr to PG full text because of strange syncing issues and it was just as fast.
Afterwards I never really saw the point of any of the search systems other than elastic search because the streaming capabilities that it gives you.
I don't have a lot of experience with PG full text search in production or a bigger scale. I'd just suspected that it doesn't perform that well if you need a lot of filters, range queries etc? Maybe someone with more experience can chime in.
At work we just materialize the data from PG into ES and take advantage of the powerful ES queries and redundancy. Scaling up by just adding nodes is easier.
For our use case we don't really need a strict verification. Everything that should go to ES is put in a queue (using DB triggers) and then sent off to ES. If something should cause errors we'd see that by observing the error rate on ES ingest I assume.
Depends on the use case. We use it alongside Cassandra and MySQL, but we also deal with tens of billions of records. If you just have a webblog, you could just use elastic.
Elasticsearch is incredibly deep, and highly performant. If all you need is simple full text search then rolling your own can be an interesting exercise, but I can't imagine the amount of hours it would take to replicate the features I use on a daily basis.
> Elasticsearch is incredibly deep, and highly performant
For some value of "highly performant". I remember its search (exact substring
match) being significantly slower than simply running grep on the same data
(JSON documents produced from syslog logs) stored in flat files.
It did have several advantages over grep in that scenario (e.g. having
a structured language and being accessible for other programs through
network), but performance was not one of them.
Right: my experience is with a lot more complex scenarios, and in comparison to rdbms. Things that would take multiple queries, like aggregations, can be done in a single, fast, call. It does require a proper indexing setup and some fine tuning though.
The simple answer is because Elasticsearch has had thousand of hours already put into writing its code base. The real question should be why shouldn't you use Elasticsearch? Is the code base to large to fit where you need it to be? Will it be able to scale with your project? Is it efficient enough for your requirements? When looking to use a piece of technology the requirements and long term effects are what matter. Roll your own if that is what is required for you to reach your end goal.
Seriously, creating an efficient scalable search engine is among the most difficult computer science problems. From stemming, to combined queries, to word tokenization, to handling various string collations, and language issues, and caching, and parallelization of work, and handling huge numbers of writes, there are so many tricky parts. I used to work for a search startup and I can answer the OP's question: do not try to write your own search engine. That work should be done by someone with a PhD and decades of experience. Even elastic which is great software has issues, such as not being transactional, having issues handling huge numbers of writes, ect.
It depends on the complexity, which language(s) you are using, and how you will parse the search string (which Context Free Grammar it will obey). Just recently, this was a task for me that ended up landing me an interview. A take-home problem. Even a simple search engine needs some clever reverse-indexing for speed. Add any sort of logic like And or OR that not even Google implements and now your parser has to work, and you have to be able to translate from tokenized parse tree with operators to results. It's a great learning exercise for someone with experience, initiative, and enough background with CFG parsing, building a reverse index, and set logic -- but without some key Computer Science building blocks it would end up being quite a challenge.
Actually, the origin story of Elasticsearch started with Shay Banon attempting to build a cooking app for his wife who was a chef.
> JAXenter: You started Compass, your first Lucene-based technology, in 2004. Do you remember how and why you became interested in Lucene in the first place?
> Shay Banon: Reminiscing on Compass birth always puts a smile on my face. Compass, and my involvement with Lucene, started by chance. At the time, I was a newlywed that just moved to London to support my wife with her dream of becoming a chef. I was unemployed, and desperately in need of a job, so I decided to play around with “new age” technologies in order to get my skills more uptodate. Playing around with new technologies only works when you are actually trying to build something, so I decided to build an app that my wife could use to capture all the cooking knowledge she was gathering during her chef lessons.
> I picked many different technologies for this cooking app, but at the core of it, in my mind, was a single search box where the cooking knowledge experience would start a single box where typing a concept, a thought, or an ingredient would start the path towards exploring what was possible.
> This quickly led me to Lucene, which was the defacto search library available for Java at the time. I got immersed in it, and Compass was born out of the effort of trying to simplify using Lucene in your typical Java applications (conceptually, it simply started as a “Hibernate” (Java ORM library) for Lucene).
> I got completely hooked with the project, and was working on it more than the cooking app itself, up to a point where it was taking most of my time. I decided to open source it a few months afterwards, and it immediately took off. Compass basically allowed users to easily map their domain model (the code that maps app/business concepts in a typical program) to Lucene, easily index them, and then easily search them.
> That freedom caused people to start to use Compass, and Lucene, in situations that were wonderfully unexpected. Imagine already having the model of a Trade in your financial app, one could easily index that Trade using Compass into Lucene, and then search for it. The freedom of searching across any aspect of a Trade allowed users to convey this freedom to their users, which proved to be an extremely powerful concept.
> Effectively, this allowed me to be in the front seat of talking and working with actual users that were discovering, as was I, the amazing power that search can have when it comes to delivering business value to their users. Oh, and btw, my wife is still waiting for that cooking app.
Now, 10 years later, it is the basis of Elasticsearch.
Yeah I'm not sure what the OP is talking about. Lucene is the java search library that Elastic uses. Elastic is a full clustered search engine with HA, sharding, and a rest api. They aren't exactly interchangeable.
Definitely build it completely from scratch. You should roll your own TCP libraries as well. Don't trust anything you didn't write yourself. Come to think of it, I'm not sure you should rely on someone else's hardware either.
Use HN to poll for opinions and experiences from others, not for things that take 30 minutes to resolve.