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.
In my cassandra database I have a table with users and I want a function to search for users by their unique usernames. For that I need to query all usernames from the user table so that I can filter them serverside, because for input of "nark" I should also find username "Mark", "Narkis" and so on, so I can't just use the username as a partition key and search for the exact value.
If I give them all in the same partition, it results in a hot partition. If I distribute them over multiple partitions, I have to search in all of them.
How can query that efficiently for millions of users? Is there a way to search like that without querying all usernames?
Thank you for your help!
Cassandra natively is not a good fit for such a use case. Even extensive use of secondary indexes will be of minimal help here.
Nevertheless if you already have all your data on C* to achieve such a functionality you essentially need a indexing framework on top of it, most widely used is Apache SOLR (built on Lucene).I have seen SOLR work like magic for fuzzy searching on C* though nothing beats having something like Elasticsearch for the use case.
We have a SAAS product where companies create accounts and populate their own private data. We are thinking about using ElasticSearch to allow the customer to search all their own data in our system.
As an example we would have a free text search where the user can type anything and the API would return multiple different types of objects. E.g. they type John and the API returns the user object for users matching a first name containing John, or an email containing John. Or it might also return a team object where the team name matches John (e.g. John's Team) etc.
So my questions are:
Is ElasticSearch a sensible choice for what we want to do from a
concept perspective?
If we did use ElasticSearch what would be the
best way to index the data so we can search all data for a
particular customer? Does each customer have its own index?
Are there any hints on how we keep ElasticSearch in sync with the data in the database (DynamoDB)? If we index the data for a customer and then update the data as it changes is it sensible to then also reindex the data on a scheduled basis too?
Thanks!
I will try to provide general answers from my own experience with splitted customer data with elastic search:
If you want to search through a lot of data really fast, ES is always a really good solution for this - it comes with the cost of an secondary data storage that you will have to keep in sync with your database.
You cant have diffrent data types in one index, so the case would be either to create one index per data type and customer (carefull, indices come with an overhead - avoid creating too much with little data in it) - or you create one index per data type and add a property to your data where you then can filter it with e.g. a customer number.
You will have to denormalize your data as much as possible to benefit from elastic search.
As mentioned in 1 you will need to keep both in sync - there are plenty ways too do that. As an example we use a an event driven approach to push critical updates into elasticsearch as soon as possible (carefull: its not SQL - so you will always have some concurrency issues when u need read and write safety). For data that is not highly critical we use jobs that update them regulary. When you index a document with the same id it will get completely updated.
Hope this helps, feel free to asy questions.
I am new to elastic and starting to sync my database tables into elastic indexes. I have started by using the table ID(UUID) as the elastic id, but I am starting to wonder if this is a mistake in terms of performance or flexibility in the long term? Any advice would be appreciated.
I think this approach should actually be a best practice. When you update data in your ES index from the (changed) DB, you can address the document directly.
It has worked great for us to use the _bulk update API, which requires an explicit id per item.
On every change on the DB side, we enqueue change notifications, the changed object gets JSON-serialized and sent to ES, asynchronously, and in larger batches. That is making a huge performance difference. Search performance, on the other side, does not depend on the length of the _id AFAIK, not even when you look up by _id. So your DB UUID should be just fine. Especially since _ids can be alphanumeric, they are not limited to just numbers.
Having a 1:1 relationship via _id between the ES result and your system of record (I assume that's what your DB is for) is advantageous also for transparency purposes. In any case, you want to store the database ID as some field, ideally indexed, at least, to help you understand where that document came from.
So, rather than creating your own ID field, you may as well use the built-in _id field right away, with your DB-supplied data.
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.