I am learning NoSQL and looking at different options for one of my client's requirements. I have gone through various resources before putting up this question (a person with little knowledge in NoSQL)
I need to store data at faster rate and read data.
Fully fail-safe and easily scalable.
Able to search through data for Analytics.
I ended up with a short list of: Cassandra and Elasticsearch
What I do understand is Cassandra is a perfect NoSQL storage solution for me, as I can write data and read data using indexes. Where it fails or it could fail is on Analytics. In the future, if I want to get data from from_date to to_date, or more ways to get data for analytics, if I don't design the Data model properly or keeping long term sight, which might be quite hard in ever changing world.
While Elastic Search is best at indexing (backed by Lucene), and can search the data randomly by throwing some random text. But does it work the same for even if I want to retrieve data from_date to to_date (I expect it might be). But the real question is, is it a Search Engine, or perfect NoSQL data storage like Cassandra? If yes, why do we still need Cassandra?
If both of these are in different world, please explain that! How do we combine them to get a more effective solution?
One of our applications uses data that is stored into both Cassandra and ElasticSearch. We use Cassandra to access those records whenever we can, and have data duplicated into query tables designed to adhere to specific application-side requests. For a more liberal search than our query tables can allow, ElasticSearch performs that functionality nicely.
We have asked that same question (of ourselves)..."Why don't we just get everything from ElastsicSearch?"
The answer is that ElasticSearch was designed to be a search engine, and not a persistent data store. Sometimes ElasticSearch loses writes. Schema changes are difficult to do in ElasticSearch without blowing everything away and reloading. For that purpose, I have written jobs that are designed to keep ElasticSearch in-sync with our Cassandra cluster. There was also a fairly recent discussion on Quora about this topic, that yielded similar points.
That being said, ElasticSearch works great as a search engine. And Cassandra works great as a scalable, high-performance datastore. But querying data is different from searching for data. There are times that we need one or the other, and a combination of the two works well for our application. It may (or it may not) work well for yours.
As for analytics, I have had some success in using the Cassandra Spark connector, to serve more complex OLAP queries.
Edit 20200421
I've written a newer answer to a similar question:
ElasticSearch vs. ElasticSearch+Cassandra
Cassandra + Lucene is a great option. There are different initiatives for this issue, for example:
Stratio’s Cassandra Lucene Index - Derived from Stratio Cassandra, is a plugin for Apache Cassandra that extends its index functionality. (https://github.com/Stratio/cassandra-lucene-index)
Stratio Cassandra, it's a native integration with Apache Lucene, it is very interesting. (https://github.com/Stratio/stratio-cassandra) - THIS PROJECT HAS BEEN DISCONTINUED IN FAVOUR OF Stratio’s Cassandra Lucene Index
Tuplejump Calliope, it's like Stratio Cassandra, but it's less active. (https://github.com/tuplejump/stargate-core)
DSE Search by Datastax. It allows using Cassandra with Apache Solr, but it's a proprietary option.(http://www.datastax.com/what-we-offer/products-services/datastax-enterprise)
After working on this problem myself I have realized that NoSQL databases like casandra are good when you want to make sure you are preserving your data schema with reliable writing operation, and don't want to take advantage of indexing operations that elasticsearch offers. In case you want to preserve some indexes data then elasticsearch is good in case you are trusting your scheme and only going to do far more reads than writes.
My case was data analytics. So I preserved a lot of my Latices in elastic search since later I wanted to traverse through the data a lot to see what should be my next step. I would have used casandra if I wanted to have a lot of changes in the schema of the data in my analytic pilelines.
Also there are many nice representing tools like kibana that you can use to present your data with some good graphics. Maybe I am lazy but they are very good looking and they helped me.
Storing data in a combination of Cassandra and ElasticSearch gives you most functionality. It allows you to lookup key-value tables, and also allows you to search data in indexes.
The combination gives you a lot of flexibility, ideal for your application.
Elassandra is the combined solution of Cassandra + Elastic search , It uses Elastic search to index the data and Cassandra as the data store , i'm not sure about the performance but as per this article , its performance is good.
If your application needs search feature then , Elassandra is the best open source option. DSE search is available but its expensive.
We had developed an application where we used Elasticsearch and Cassandra.
Similar data was stored into Cassandra and indexed into Elasticsearch.
Our application's UI was having features like searches, aggregations, data export, etc.
The back-end microservices were continuously getting huge data (on Kafka topics) and storing it into Cassandra. Once the data is stored into Cassandra, the services would make sure the data is indexed into Elasticsearch.
Cassandra was acting as "Source of truth" for Elasticsearch. In the cases, where reindexing of the ES index was required, we queried Cassandra and reindexed the data into ES.
This solution helped us, as this was very easy to scale and the searches and aggregations were much faster.
Cassandra is great at retrieving data by ID. I don't know much about secondary index performance, but I doubt it's as fast as Elasticsearch. Certainly Elasticsearch wins when it comes to full text search functionality (text analysis, relevancy scoring, etc).
Cassandra wins on update performance, too. Elasticsearch supports updates, but an update is really a reindex + soft delete in an atomic operation.
Cassandra has a very nice replication model (if you need to be extra-fail-safe). Elasticsearch is OK, too, I'm not in the camp that says ES is particularly unreliable (it has issues sometimes, like all software).
Elasticsearch also has aggregations for real-time analytics. And because searches are so fast, analytics on a subset of data will be fast, too.
If your requirements are satisfied well enough by one of them (like here it seems like ES would work well), I would just use one. If you have requirements from both worlds, then you can either:
use one of them and work around the downsides. For example, you may be able to handle many updates with Elasticsearch, but with more shards and more hardware
use both and make sure they're in sync
As elasticsearch is built on Lucene index and if you want to store indexing in elasticsearch it performs best comparing to indexing in Cassandra itself for retrieving the data.
If your requirements are not related to real-time retrieval then you can use elasticsearch as NoSQL database also, there are thoughts that ElasticSearch loses writes & Schema changes are difficult, but if your volume of data is not too big. You can easily achive elasticsearch as a search engine with best indexing along with elasticsearch as aNoSQL database. There are several way that you can prevent it. I have worked on the schema changes in elasticsearch, if your data structure is consistent then it will create any issues.
Being a supporter of ElasticSearch or SOlr. I have worked on both the search engines and i experienced that both the search engines can be used fluently if you configure them correctly.
Only cons that i can think of it, if you are targetting real time result and can't comprosie milliseconds delay in your response. Then its better to take help of other NoSQL databases like cassandra or couchbase.
Cassandra with solr, work better than Cassandra with elasticSearch.
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
I am pretty new to elasticsearch and couchbase and I want to understand a few concepts in order to make decisions on how to use them in my applcation.
My overall question: is it possible to configure bidirectional xdcr between couchbase and elasticsearch using the transport-couchbase plugin?
Since it is possible to update documents that are in an elasticsearch index, I want to be able to propagate that update back to the couchbase server. Is there any way to do this? Ive searched online for anything that can do this, with no luck so far...
as far as I know it's currently not possible to sync changes from Elasticsearch to Couchbase.
From a semantic perspective, you probably don't want to change an index, and forward a change from the index to the DB backing that index.
I maintain the Couchbase transport plugin and as Laurent answered earlier, it doesn't allow replicating from Elastic back to Couchbase. While it's technically possible to implement this functionality, it wouldn't make sense to do so in practice. The whole point of replicating to Elastic is to use it as an index on top of your source of truth, which is Couchbase. That way you get the high performance reads and writes of Couchbase and the query and search functionality of Elastic. Replicating changes back to Couchbase would mean the data there is no longer authoritative, which in turn opens the door to all sorts of concurrency and data integrity problems.
I'm just wanting to know what is exactly Elastic Search.
It is said it helps to search data but when I see some webinars it feels like I have to replicate my data in a kind of Elastic datastore... which not means very otpimized to me. In that way all modification done on left hand will have to be reported on right hand and data returned by Elastic Search may not be in the right format.
Can Elastic Search can directly search in my database?
It's to use with a Neo4J graph database. Does somebody already did something like that? Does that only replace the Cypher queries?
Thanks for advices, helping me on realize on what Elastic Search can really helps on our project.
Elasticsearch is a database, however it's not a relational database like you may be used to. It is a NoSQL database.
You insert JSON documents into an index. You query that index to find documents that match a particular criterion.
It is also sharded and node distributed, which gives it resilience and scalability, and also - if you set it up right - performance.
This means it's really good at 'search engine' style database queries, but because it's not relational, it cannot do the equivalent of a SQL JOIN operation very easily.
One example use case is logstash and kibana - known as the ELK stack - where system event logs (syslog, httpd logs, that kind of thing) are processed by logstash to parse metadata - like log source, referrer, URL, session ID, etc. - and then inserted into elasticsearch.
As each event is a self contained piece of information, this is what elasticsearch does particularly well.
You can then use Kibana as a visualisation engine to display your logs, but also perform analysis - most hit pages, geographic distribution of requests, incoming referrers, time based distribution of requests, etc.
But it also collates these logs, so if you run a really large, geographically distributed website with multiple webserver nodes - or maybe you just have a lot of servers in your computer room and want to summarise the system logs - you can feed the whole lot into elastic search.
It's design is such that it's good at handling near-real-time data insertion and analysis. It also works quite well for 'forum style' data models, as essentially all you're doing is querying a list of posts with a particular forum name, and finding replies to a particular parent node - but they're standalone 'documents'.
So yes, you probably could use it to search an existing database, but you'll have to think about your data model - you can't just translate a conventional relational model, you would have to flatten it. Denormalisation is something of a sin in RDBMS terms, but it's actually quite good for search engines, because you can execute queries in parallel more efficiently.
There exist some way to combine both approaches. Have a look at this blog post:
http://graphaware.com/neo4j/2015/09/30/recommendations-with-neo4j-and-graph-aided-search.html
Databases cannot be optimized for all use cases, but luckily there are many databases available so we can choose the best one for each task.
Elasticsearch is optimized for:
Filtering of documents (exact match)
Search ranking of documents (relevance of search terms)
Aggregation of results (sums, distinct counts, percentiles, ...)
Neo4j is optimized for:
Graph traversal (naturally)
High performance when operated on a "local" graph neighborhood (context)
Actually both databases use the same underlying library Lucene to "index" data to be searched later.
ES is an open source, distributed, RESTful, JSON-based search engine. It is easy to use, scalable and flexible. The indexing feature helps in fast retrieval of search queries.
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.