I have 2 indexes and they both have one common field (basically relationship).
Now as elastic search is not giving filters from multiple indexes, should we store them in memory in variable and filter them in node.js (which basically means that my application itself is working as a database server now).
We previously were using MongoDB which is also a NoSQL DB but we were able to manage it through aggregate queries but seems the elastic search is not providing that.
So even if we use both databases combined, we have to store results of them somewhere to further filter data from them as we are giving users advanced search functionality where they are able to filter data from multiple collections.
So should we store results in memory to filter data further? We are currently giving advanced search in 100 million records to customers but that was not having the advanced text search that elastic search provides, now we are planning to provide elastic search text search to customers.
What do you suggest should we use the approach here to make MongoDB and elastic search together? We are using node.js to serve data.
Or which option to choose from
Denormalizing: Flatten your data
Application-side joins: Run multiple queries on normalized data
Nested objects: Store arrays of objects
Parent-child relationships: Store multiple documents through joins
https://blog.mimacom.com/parent-child-elasticsearch/
https://spoon-elastic.com/all-elastic-search-post/simple-elastic-usage/denormalize-index-elasticsearch/
Storing things client side in memory is not the solution.
First of all the simplest way to solve this problem is to simply make one combined index. Its very trivial to do this. Just insert all the documents from index 2 into index 1. Prefix all fields coming from index-2 by some prefix like "idx2". That way you won't overwrite any similar fields. You can use an ingestion pipeline to do this, or just do it client side. You only will ever do this once.
After that you can perform aggregations on the single index, since you have all the data in one-index.
If you are using somehting other than ES as your primary data-store you need to reconfigure the indexing operation to redirect everything that was earlier going into index-2 to go into index-1 as well(with the prefixed terms).
100 million records is trivial for something like ELasticsearch. Doing anykind of "joins" client side is NOT RECOMMENDED, as this will obviate the entire value of using ES.
If you need any further help on executing this, feel free to contact me. I have 11 years exp in ES. And I have seen people struggle with "joins" for 99% of the time. :)
The first thing to do when coming from MySQL/PostGres or even Mongodb is to restructure the indices to suit the needs of data-querying. Never try to work with multiple indices, ES is not built for that.
HTH.
I'm creating a microservice to handle the contacts that are created in the software. I'll need to create contacts and also search if a contact exists based on some information (name, last name, email, phone number). The idea is the following:
A customer calls, if it doesn't exist we create the contact asking all his personal information. The second time he calls, we will search coincidences by name, last name, email, to detect that the contact already exists in our DB.
What I thought is to use a MongoDB as primary storage and use ElasticSearch to perform the query, but I don't know if there is really a big difference between this and querying in a common relational database.
EDIT: Imagine a call center that is getting calls all the time from mostly different people, and we want to search fast (by name, email, last name) if that person it's in our DB, wouldn't ElasticSearch be good for this?
A relational database can store data and also index it.
A search engine can index data but also store it.
Relational databases are better in read-what-was-just-written performance. Search engines are better at really quick search with additional tricks like all kinds of normalization: lowercase, รค->a or ae, prefix matches, ngram matches (if indexed respectively). Whether its 1 million or 10 million entries in the store is not the big deal nowadays, but what is your query load? Well, there are only this many service center workers, so your query load is likely far less than 1qps. No problem for a relational DB at all. The search engine would start to make sense if you want some normalization, as described above, or you start indexing free text comments, descriptions of customers.
If you don't have a problem with performance, then keep it simple and use 1 single datastore (maybe with some caching in your application).
Elasticsearch is not meant to be a primary datastore so my advice is to use a simple relational database like Postgres and use simple SQL queries / a ORM mapper. If the dataset is not really large it should be fast enough.
When you have performance issues on searches you can use a combination of relation db and Elasticsearch. You can use Elasticsearch feeders to update ES with your data in you relational db.
Indexed RDBMS works well for search
If your data is structured i.e. columns are clearly defined, searching 1 million records will also not be a problem in RDBMS.
When to use Elastic
Text Search: Searching words across multiple properties (e.g. description, name etc.)
JSON Store and search: If data being stored is in json format and later needs to be searched
Auto Suggestions: Elastic is better at providing autocomplete suggestions
Elastic as an application data provider
Elastic should not be seen as data store, even if you storing data in it. It is about how you perceive elastic. Elastic should be used to store and setup data for the application. It is the application which decides how and when to use elastic (search and suggestions). Elastic is not a nosql storage alternative if compared to RDBMS, you should use a nosql database instead.
This perception puts elastic in line with redis and kafka. These tools are key components of an application design and they are used to serve as events stores, search engines and cache etc. to the applications.
Database with Elastic
Your design should use both. For storing the contacts use the database, index the contacts for querying. Also make the data available in elastic for searching, autocomplete and related matches.
As always, it depends on your specific use case. You briefly described it, but how are you acually going to use the data?
If it's just something simple like checking if a customer exists and then creating a new customer, then use the RDMS option. Moreover, if you don't expect a large dataset, so that scaling isn't an issue (hence the designation that Elasticsearch is for BigData), but you have transactions and data integrity is important, then a RDMS will be the right fit. Some examples could be for tax, leasing, or financial reporting systems.
However, if you have a large dataset, you need a wide range of query capabilities, such as a fuzzy search or searches where the user
can select multiple filters on the data or you want to do some predictive analysis on the data, then Elasticsearch is the clear choice.
For example, I worked on an web based app with a large customer base: 11 million, with 200+ hits per second at peak time for a find a doctor application. The customer could check some checkboxes to determine, specialty, spoken languages, ratings, hospitals, etc. all sorted by the distance from the users location with a 2 second or less response time. It would be very difficult for a RDMS to match that.
I am working on a large scale oracle database. There is a requirement to validate whether the email address is available in the database when user enters it. If a direct database call is made it would be exact match
e.g Select email from Users where emailaddress = "sampleemail#domain.com" it is not a LIKE.
It has been suggested that rather than doing a direct database exact search it would be better to do a solr search for this. Even so it will be an exact match.
I would like understand, Can there be a significant advantage in using solr in this scenario as it is a exact match. If so how
No, don't do that. Build an index in the database (a unique btree) and query that. Whoever suggested solr for this is highly misinformed about the trade offs. This is literally and exactly why there are indexes inside the database at all.
I have a collection of OWL ontologies. Each ontology is stored in a dataset of a triple store database (e.g OWLIM, Stardog, AllegroGraph ). Now I need to develop an application which supposes searching these ontologies based on keywords, i.e., given a keyword, the application should return ontologies that contains this keyword.
I have checked OWLIM-SE and Stardag, they only provide full text search over one dataset but not the whole database. I also have considered Solr(Lucene). But in this case the ontologies will be indexed twice (once by Lucene, another one by triple store database.)
Is there any other solution for this problem?
Thanks in advance.
Stardog's full text indexing works over an entire database and can be done transparently with SPARQL which will allow you to easily access other properties of the concepts matching your search criteria in a single query. This will get you precisely what you're describing.
For some information on administering the search indexes, and Stardog in general, check out these docs
I've been playing around with apache's cassandra project. Done a fair bit of readin and i have some fairly complex examples that i've done, including inserting single and batch sets of data, retrieving a single and multiple data sets based on keys.
Some of the articles i've looked at include
http://www.rackspacecloud.com/blog/2010/05/12/cassandra-by-example
http://github.com/digg/lazyboy
http://arin.me/blog/wtf-is-a-supercolumn-cassandra-data-model
http://www.sodeso.nl/?p=80
I've got a fairly good grasp of the concepts explained and have even implemented a simple app.
None of the articles describe how one would go about performing a query where, for eg, the query is a search term a user has typed in.
Does anyone know how or can suggest how i'd go about performing such a query?
Or perhaps a way to create a searchable index, full text search or anything even remotely close?
You will probably split text into words, and than use these words as keys to your "index". Each word will contain timestamp ordered column family with list of IDs to your articles, messages etc. So you can only perform simple searches over keys (words).
When searching more than one word, use intersection over these column families.
This is very simple approach, if you need more complex queries look at Lucandra - http://github.com/tjake/Lucandra - Lucandra is a fulltext search engine with Cassandra as backend storage.