I have used Algolia a bit and it is an awesome service. I have also used the TNT-search driver for scout and it is also pretty good, but not really a touch on the features, speed and ease that you get with Algolia.
Unfortunately, Algolia gets very expensive when dealing with a lot of records - for example one of our apps has over 10 million searchable rows, which would be thousands of $$ monthly!
Has anyone had any success in using both together? i.e. I would like use Algolia for recent records or categories where I need more advanced search capabilities (100k of records) and then use TNT search for the remainder.
EDIT:
Elasticsearch was the answer. It is a little harder to setup but has such great flexibility. I would highly recommend https://github.com/matchish/laravel-scout-elasticsearch to connect it up with Laravel Scout seamlessly.
Well ,AWS Elasticsearch very good and cheap .. you might use it and it's very easy to use and configure with laravel AWS Elasticsearch pricing
You may start with r3.large.elasticsearch it will costs you about 180$ per month and if you want to more r3.xlarge.elasticsearch will be amazing too and it will serve you need.
to configure AWS Elasticsearch with laravel you may read this artical How-to integrate-your-Laravel-app-with-Elasticsearch
Elasticsearch is already suggested in another answer as a cost effective alternative. But if you are looking for something similar to Algolia but open source, check out Typesense. It's must easier to set-up and manage and also features like typo correction etc. just works out of the box.
This answer might be late but if there is anyone who still has this problem, Laravel scout has a searchableUsing() method that determines the search engine to use, you can override that to configure different search drivers for different models. This blogpost is a detailed step by step on how to do this
Related
I am a member of an Analytics team that recently moved it's Data Warehouse into Elastic Search. The DW is accessed through Dremio.
However, I am having second thoughts regarding whether Elastic Search is the appropriate DB for an Analytics team that performs a lot of day-to-day Analytics. I would prefer we kept our DW in one of BigQuery/Snowflake/Redshift and use "dbt" tool for transforming data and writing it back into the DB.
I can't find a "dbt"-like tool to perform quick data transformations after reading from Elastic Search and Dremio is not mature enough tool for that. I would like to solicit your thoughts on Elastic Search and whether is an appropriate DB for day-to-day analytics.
I appreciate your responses.
Edit:
I work at an online retailer. Our data is not "big data" in any sense. In the order of a few thousand orders per day. Most of our work is responding to inquiries from various teams/departments. Some of these questions go beyond a simple query. We have to build customized data marts that involve multiple steps in between. As a result, we need a tool that would allow us to transform data quickly and put the result set into a database. One such tool is "dbt" but it doesn't support Elastic Search. So the question is whether there is an appropriate tool for this job or Elastic Search is not appropriate for our use case.
Taking into account
Our data is not "big data" in any sense.
most likely ElasticSearch is not appropriate choice. Only reason to use ES is a lot of search-like queries with 'contains' filtering over text-type fields and only if dataset is too large for fast-enough handling of these queries by SQL-compatible DB.
It looks like PostgreSQL can do the job. If you're looking for columnar-DB for lighting-fast OLAP queries (aggregations) you can check open-source ClickHouse.
Finally, Dremio is not the only BI tool that can work with ElasticSearch (or PostgreSQL, ClickHouse etc). Some BI tool allows you to use 'painless' scripts for dimensions/measures and you can calculate a lot of things directly in ES queries.
Depends on what specific metrics you need, ES aggregation can support a lot of basic metrics. For cost considerations and less infra to support and reducing complexity, I generally advise companies to start with that before over engineering or prematurely optimizing
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html
I'm currently designing the architecture of my project or atleast try to figure it out what will be useful in my case.
** Simple use case
I will have several thousands of profiles in a backend and I to need implement a fast search engine. So elasticsearch look perfect in that case. Everytime a profile is updated, the index will be updated by an asynchronous task.
My question now is : If I want to implement a cache system for the detail of a profile. Should I stick with elasticsearch and put these data in my index ? Or use Redis and do something like profil_id => data ?
I think both sounds good the problem is whenever a profile is updated, I will have to flush it after the reindexing in elasticsearch. If I want to see the change in my backend.
So what can I do ? Thank you so much !
You should consider using RediSearch. Using RediSearch can provide you a solution for your needs, getting both Redis performance and a full-text support.
Elasticsearch and redis are basically meant to solve two different problems, As one does indexing while other does caching.
Redis is meant to return already requested data as fast as possible whereas as
Elasticsearch is a search and analytics engine, it would perfectly fit a use-case where you have to implement a fast search engine and it will be more performant than any in-memory data structure store or cache such as redis(Assuming your searches will be complex, will involve some aggregation/filters).
The problem comes profile updates Since your profile updates are not that frequent you could actually do partial updates to the ES index rather doing reindex.So whenever a person updates its profile get the changeling set(changed data) and do a partial update to the particular document in ES Index. You can see how its done here partial update.
This one particular stackoverflow answer will help you cache vs indexing
We are planning to introduce Elastic search(AWS) for our Multi tenancy application. We have below options,
Using One Index Per Tenant
Using One Type Per Tenant
All Tenants Share One Index with Custom routing
As per this blog https://www.elastic.co/blog/found-multi-tenancy the first option would give memory issue. But not clear about other options.
It seems if we are using the third option then there is no data segregation. Not sure about security.
I believe second option would be better option as data would be segregated.
Help me to identify best option to proceed elastic search with Multi tenancy.
Please note that we would leverage AWS infrastructure.
We are considering the same question right now, and the following set of articles by Elasticsearch was very helpful.
Start here: https://www.elastic.co/guide/en/elasticsearch/guide/current/scale.html
And read through each subsequent article until you hit this one: https://www.elastic.co/guide/en/elasticsearch/guide/current/finite-scale.html
The following two were very eye-opening for me:
https://www.elastic.co/guide/en/elasticsearch/guide/current/faking-it.html
https://www.elastic.co/guide/en/elasticsearch/guide/current/one-big-user.html
The basic takeaway:
Alias per customer
Shard routing
Now you can have indexes for big customers, shared indexes for little customers, and they all appear to be separate indices
This is a too important link not to be mentioned here:
http://www.bigeng.io/elasticsearch-scaling-multitenant/
Good architecture dilemmas, and great performance analysis / reasoning.
tldr; they had index groups that are built around shard allocation filtering to segregate load across nodes in the cluster
To sum up accepted answer and other articles,
Use a shared index using custom routing using an alias
1.1) Special case: Big client can have dedicated index, only if needed.
Following article covers many use cases for detailed explanation.
https://www.elastic.co/blog/found-multi-tenancy
Following is the conclusion on how you can do it (link source: accepted answer)
https://www.elastic.co/guide/en/elasticsearch/guide/current/faking-it.html
I have been working with elasticsearch for the past 2 months. I have used both REST approach and API support in different languages to index, get and search data. I also read a lot about elasticsearch and found out it is not a good option to use it as a data store. Why is this? And I'm also curious about how elasticsearch internally stores the indexed data. Any good link or explanation??
Elastic Search is built on top of Apache Lucene - here's a reference doc on the Lucene index file structure:
http://lucene.apache.org/core/4_7_2/core/org/apache/lucene/codecs/lucene46/package-summary.html#package_description
Regarding whether or not it's a good option as a data store I think that's more individual opinion and specific use cases than a fact that can be proved. It does not have the transaction support that something like MySQL does if that's what you are looking for. In that case it's somewhat on a par with other NoSQL solutions. This is a pretty decent writeup on the trade-offs and issues: https://www.found.no/foundation/elasticsearch-as-nosql/
In the end it depends on what you are doing with your data and what level of robustness you require.
In Shay's Berlin Buzzwords talk, in the "user data flow" portion, he talked about "very large users can be migrated to their own respective indices". Is this said migration a out-of-the-box supported operation with no user-visible disruption, or is it a custom migration that we'll have to implement ourselves?
As Mike W said, we need more information to give you a better answer.
But I think you are trying to migrate that specific user to his own index. If so, we should be using the alias feature elaticsearch gives you out-of-the-box. You can read more of it at this link
http://www.elasticsearch.org/blog/changing-mapping-with-zero-downtime/
I believe you need a mapping to support your idea. Mapping each user to the corresponding index, so very large users will go to their own index and others not so large can share the same index.