I am preparing to build an Android/iOS app that will require me to make complex polygon and containment geospatial queries. I like Apache Cassandra's no single point of failure, fault tolerance and data center awareness. Cassandra does not have direct support for geospatial queries (that I am aware of) but MongoDB and Couchbase Server do. MongoDB has scaling issues and I'm not sure if Couchbase would be a better alternative than Cassandra with Solr or Elasticsearch.
Would I be making a mistake by going with Datastax Enterprise (DSE), Cassandra and Elasticsearch over Couchbase Server? Will there be a noticeable difference in load times for web pages with the Cassandra/ES back end vs. Couchbase?
Aerospike just released Server Community Edition 3.7.0, which includes Geospatial Indexes as a feature.
Aerospike can now store GeoJSON objects and execute various queries, allowing an application to track rapidly changing Geospatial objects or simply ask the question of “what’s near me”. Internally, we use Google’s S2 library and Geo Hashing to encode and index these points and regions. The following types of queries are supported:
Points within a Region
Points within a Radius
Regions a Point is in
This can be combined with a User-Defined Function (UDF) to filter the results – i.e., to further refine the results to only include Bars, Restaurants or Places of Worship near you – even ones that are currently open or have availability. Additionally, finding the Region a point is in allows, for example, an advertiser to figure out campaign regions that the mobile user is in – and therefore place a geospatially targeted advertisement. Internally, the same storage mechanisms are used, which enables highly concurrent reads and writes to the Geospatial data or other data held on the record. Geospatial data is a lot of fun to play around with, so we have included a set of examples based on Open Street Map and Yelp Dataset Challenge data.
Geospatial is an Experimental feature in the 3.7.0 release. It’s meant for developers to try out and provide feedback. We think the APIs are good, but in an experimental feature, based on the feedback from the community, Aerospike may choose to modify these APIs by the time this feature is GA. It’s not intended for Production usage right now (though we know some developers will go directly to Production ...)
Aerospike provides a proven highly scalable NoSQL solution. Geospatial query has recently been added, and an Early Adopter release has just been announced. You might want to check that out.
Redis is probably one of the best alternatives. At the current time you would need to use Redis Unstable 3.2. The performance is oustanding. I have been using this with the lettuce java client and have seen incredible results. The larger the radius will decrease performance.
http://redis.io/commands/geohash
You are asking quite a few questions, as has been pointed out. The provided link offers one potential answer to how generic geospatial operations could be implemented using Cassandra. I'll offer one possible answer using straightforward out-of-the-box Cassandra constructs.
Using geohashes (or quad trees), or something similar, create an index of geohashes and their associated polygons. The specific relationship and level(s) of precision are dependent on your data set and use case.
To determine which polygons intersect with a given point or polygon, first compute its geohash(es), then look those geohashes up in the index. For general proximity, this may be sufficient. Either way, this narrows the potential intersection points down to a manageable set.
Related
I've been evaluating Cassandra to replace MySQL in our microservices environment, due to MySQL being the only portion of the infrastructure that is not distributed. Our needs are both write and read intensive as it's a platform for exchanging raw data. A type of "bus" for lack of better description. Our selects are fairly simple and should remain that way, but I'm already struggling to get past some basic filtering due to the extreme limitations of select queries.
For example, if I need to filter data it has to be in the key. At that point I can't change data in the fields because they're part of the key. I can use a SASI index but then I hit a wall if I need to filter by more than one field. The hope was that materialized views would help with this but in another post I was told to avoid them, due to some instability and problematic behavior.
It would seem that Cassandra is good at storage but realistically, not good as a standalone database platform for non-trivial applications beyond very basic filtering (i.e. a single field.) I'm guessing I'll have to accept the use of another front-end like Elastic, Solr, etc. The other option might be to accept the idea of filtering data within application logic, which is do-able, as long as the data sets coming back remain small enough.
Apache Cassandra is far more than just a storage engine. Its design is a distributed database oriented towards providing high availability and partition tolerance which can limit query capability if you want good and reliable performance.
It has a query engine, CQL, which is quite powerful, but it is limited in a way to guide user to make effective queries. In order to use it effectively you need to model your tables around your queries.
More often than not, you need to query your data in multiple ways, so users will often denormalize their data into multiple tables. Materialized views aim to make that user experience better, but it has had its share of bugs and limitations as you indicated. At this point if you consider using them you should be aware of their limitations, although that is generally good idea for evaluating anything.
If you need advanced querying capabilities or do not have an ahead of time knowledge of what the queries will be, Cassandra may not be a good fit. You can build these capabilities using products like Spark and Solr on top of Cassandra (such as what DataStax Enterprise does), but it may be difficult to achieve using Cassandra alone.
On the other hand there are many use cases where Cassandra is a great fit, such as messaging, personalization, sensor data, and so on.
I'm trying to migrate our postgres database containing millions of clicks (few years click history) to more performing system. Our current analytic queries, which are running on postgres are taking forever to complete and it degrades performance of the whole database. I've been investigating possible solutions and I've decided to closely investigate 2 options:
HBase with Hadoop (mapreduce)
Cassandra with Spark
I was working with NoSQL before, however never used it for analytical purposes. At first I was a bit disapointed how little analytical query options those databases provide (missing groupBy, count, ...). After reading many articles and presentations I've found out, that I need to design my schema according how I intend to read my data and that storage layer is separated from query layer. Which adds more redundant data, however in the world of NoSQL this is not an issue.
Eventually I've found one nice grails plugin cassandra-orm, which internally encapsulates orderBy feature in cassandra counters counters. However I'm still worried about howto make this design extendable. What about the queries, that will come in the future, which I have no clue about today, how can I design my schema prepared for that ?
One option would be to use Spark, but Spark doesn't provide data in real time.
Could you give me some insight or advice what are the best possible options for bigdata analysis. Should I use combination of real time queries vs. pre-aggregated ones?
Thanks,
If you are looking at near real time data analysis, Spark + HBase combination is one of the solutions.
If you want to compromise on throughput, Solr + Cassandra combination from Datastax can be used.
I am using Solr + Cassandra from Datastax for my use case, which does not require real time processing. The performance of search option is not that great with this combo but I am OK with the throughput.
Spark+HBase combination seems to be promising. Depending on your business requirement & expertise, you can chose the right combination.
If you want the ability to analyse data in near-real-time with complete flexibility in query structure, I think your best bet would be to throw a scalable indexing engine such as Elasticsearch or Solr into your polyglot persistence mix. You could still use Cassanra as the primary data store and then index those fields you're interested in querying and/or aggregating.
Have a look at Datastax Enterprise which bundles together Cassandra and Solr. Also have a look at Solr's Stats component and its faceting capabilities. These, combined with the indexing engine's rich query language, are handy for implementing many analytics use cases.
If your data set consists of a few million records 'only', I think you'll be able to get some good response times from Solr or ES on a reasonably spec'ed cluster.
Currently we are using mongodb as our primary store for big online sales site, and currently we are focusing ourselves on big scalability among multiple machines.
Site backend is written in node.js and we are using mongoose as ODM.
I can see many blog posts which are writing about awesome cassandra DB, and I am starting to think about switching to cassandra. But still I am not sure if this is a really good decision, because I didn't found any good ODM/ORM lib for cassandra and node.js (and writing raw queries can be pain. Also writing good tested ORM/ODM can be time consuming task). So I am not sure how much benefit will I have after this switch. We are using elasticsearch as search engine, and it works excellent in combination with mongodb, and I am asking my self will do also good with cassandra.
If you have any experiance with this, it will be very helpfull.
Thank you!
Cassandra is a very nicely designed database, which can fulfill a lot of scenarios. MongoDB is also a really good DB engine. So let me just compare couple of main bullet points for you.
Always on system
Cassandra is really great when you need to provide 24x7 operations in multiple data centers. If you got more then one datacenter with multiple servers in each of them then Cassandra is great for you. Cassandra can sync writes to more than one datacenter and maintain desired data consistency across complex set ups. Recovery and re-sync is also quite easy.
On the other note MongoDB is easy to operate. If you got one data center and only couple of servers it might be a perfect fit (although global write lock might be a pain over time). In simple deployments it's easy to maintain and monitor.
Scalability
To continue the above statements - Cassandra is linearly scalable. There is, literally, no limit of how big the cluster will be. Your writes will always stay fast, while reads might become more complicated over time - depending on the structure of your data.
Denormalization of data
With Cassandra your writes and reads can be extremely fast if you will create a structure that will reflect what you need to get from your data. There is no query language (well, there is, but it's not exactly SQL) that you can use to reorganize your result set using aggregates, groupings, etc. Yes, some things are doable and some not - that is very specific to Cassandra data model. You will have to implement a lot of things on your own and write the result to the DB - i.e. counters for aggregation, different groupings, etc.
In comparison MongoDB is easy to use, easier to learn and more flexible - both for development (as knowledge curve/efforts goes) and for implementation of business logic (as time/effort is considered). That is - kind of - a reason why there are ORM engines for MongoDB and only couple (very limited) for Cassandra.
To summarize - both DBs are really good... if you will embrace their limitations. If you got only 100GB of data and you need flexible, easy to implement DB engine I would stick to MongoDB, alternatively take a look RethinkDB which have a very similar model and way better (in my personal opinion) clustering/data center replication implementation.
Cassandra is a great option for you if you will need to store TBs of data soon, deploying your apps across multiple data centers while accepting the cost of additional efforts to implement the same features and maintaining similar capabilities.
Don't take it personally that I have used the word only while describing your data set. Yes, it's not big - my company stores more than 20 TB these days... so yeah, 100GB is really not that much...
To stop everyone from pointing that I should compare some other features or point out some other differences between those two - it's just a rough, high level overview on the things I consider relevant to the problem, not a full comparison or analysis of the problem. But feel free to point out what I have missed and I will be happy to include new stuff in this answer...
According to the neo4j documentation, indexing can be done i 2 ways"
Indexing in Neo4j can be done in two different ways:
1. The database itself is a natural index consisting of its relationships of different types between nodes. For example a tree
structure can be layered on top of the data and used for index lookups
performed by a traverser.
2. Separate index engines can be used, with Apache Lucene being the default
backend included with Neo4j.
But there is no comparison which is better in what and what is better in which cases.
Which one is better and why?
Is this a data warehouse/mart or reporting database? If you have both transactions and search going against the database it might give interesting pros or cons.
Lucene exists for one reason search and it does it really well. If you have a large system with multiple services, for ultimate scalability it is always to split the services up and keep them doing their single responsibility. This would give you flexibility of using that Lucene index against other services if necessary...also if you ever got rid off neo4j, then you still have your index/search artifacts around not coupled to Neo4j.
I would look at it from the overall system architecture not just specific functionality.
Now I have a project with ads exchange service (something like google double click) and I have to pick a high-scalable database. I'm thinking about mongodb or cassandra.
Cassandra:
fit with our write-intensive system. (+)
looks hard to do aggregate(very important for analytics) (is there a good way? Just read slide about Twitter rainbird, seem good) (?)
I dont prefer java much. (-)
MongoDB:
Seem easier to do analytics. (have build-in aggregate functions) (+)
more RAM-consuming? (because of document-oriented vs key-value Cassandra) (?)
write perfomance compare to Cassandra? (?)
javascript shell and natural fit with node.js(one important part in our project) (+)
http://pastebin.com/raw.php?i=FD3xe6Jt - This article make me cautious. (-)
Can you guys help me to pick the one or answer some of my questions above
Thanks.
I don't know about Cassandra, but MongoDB has some advantages for using it for analytics: high concurrency, sharding, storing everything about an event in a single document, features like upsert and $inc.
For more detailed explanations check the following resources:
MongoDB Analytics - videos
http://blog.mongodb.org/post/171353301/using-mongodb-for-real-time-analytics
http://www.mongodb.org/display/DOCS/Use+Cases
http://www.slideshare.net/jrosoff/scalable-event-analytics-with-mongodb-ruby-on-rails
http://nosql.mypopescu.com/post/3508305955/fast-asynchronous-analytics-with-mongodb
http://blog.opengovernment.org/2011/02/24/fast-asynchronous-analytics-with-mongodb/
http://blog.10gen.com/post/4416876632/london-startup-ubervu-on-storing-5tb-of-data-in-mongodb
It depends a lot on your domain, most cases one would probably choose Mongo.
For example http://square.github.com/cube/ is built on Mongo.
Cube is an open-source system for visualizing time series data, built on MongoDB, Node and D3. If you send Cube timestamped events (with optional structured data), you can easily build realtime visualizations of aggregate metrics for internal dashboards. For example, you might use Cube to monitor traffic to your website, counting the number of requests in 5-minute intervals:
Most use cases of Cassandra draw from the need oh high availability that's the main feature of it afaik. Your needs seem to be centered around having a cheap way to shove queryable data in a scale-out DB, and mongo almost matches RDBMS in regards to querying. Mongo is also probably easier to deal with.
I think cassandra is a good fit for this problem.
You don't need to know much java to get it running (other than install java), as long as there is a client library in your chosen language.
Cassandra 0.8+ now has atomic counter support - perfect for impressions/click tracking.
You could also run hadoop on top of cassandra, giving you a proven platform for writing map reduce jobs to do analytics/aggregations and store the results back to Cassandra too.
Check out this slideshow about cassandra and hadoop: http://www.slideshare.net/jeromatron/cassandrahadoop-4399672
I hope that helps.