I have a table with something like 60gb in my DB (using ram memory) that I want to get out to other DB... It's only one table with two columns (ID and text). And the only thing that iam doing on this DB is simple write and read request (1 object each time by id). This DB should get a lot of read and write request (web application). Iam wondering should I use mongodb or elastic or maybe there are something better for me?
Sounds like a key-value store problem. Though both MongoDB and Elasticsearch should work well for it too. What are you using now — Redis or Memcached maybe?
Do you know the size of the value (text)?
I assume you have (at least) one other datastore for other workloads in your system. Maybe you can use the same for multiple tasks? Reusing operational knowledge is definitely a win.
Other than that there is not enough information to make a hard argument for any system IMO.
Related
I'll explain the use cases first.
High read rates (10000+ p/s), large dataset (lots of string codes(think promocodes) looking for matchs, strings 10 - 20chars). Needs fast response time.
First thought was memcached. However to combat downtime if memcache goes down and starts repopulating the cache from a db like mysql.... i was thinking redis for auto repopulation of cache.
Is it true that redis does not persist to the hdd but instead a flush needs to be called for it to be backed up?
My hope is to use the code string as the key making lookup super quick. Value will be an id linking it to a db record thats not needed by the api.
If i had to guess how many unique strings will be stored..... 10M + after a few months.
Iv also looked at Cassandra briefly and mongodb. Im thinking mongodb will not be enough due to it not storing entire list in memory?
Any insight into these systems is very helpful. Feel like im going around in circles.
The api is made in nodejs. (If it matters)
10K/s is definitely not a high rate for a DB like Cassandra, according that your schema is done wisely. I bet it's the same for the others.
10M unique strings per months is peanuts for modern big data systems.
Whatever big data solution you retain, you will have to design the schema acording to the type of data and operational needs.
IMO, the important ones are the following 2 questions :
What you mean by "looking for matchs"?
If you need indexing and search using substrings or regexps, you need a search engine: ElasticSearch or SOLR are great. Warning that E/S does replication and sharding but it's distribution model is still not 100% safe.
None of the systems you mentionned will provide the reactivity you seem to look for.
If you will query using static strings: a key-value store or column oriented database like Cassandra will be just the perfect fit. So all are good fit.
What is a fast response time?
With selecting the right technology and appropriate schemas all those systems will give you great response time under hundreds of milliseconds, but will it be fast enough for you?
REDIS and MemCached being in-memory will provide the faster responses.
And as a conclusion, the API being in node.js is irrelevant for the choice of your storage and indexing technology, unless you want to stick with Javascript for everything and MongoDB is more friendly for you, it can be a decent candidate depending on your search use cases.
Up to this point, I have been using MongoDB (Node.js + Mongoose) to save posts which belong to a user, so that I can later retrieve them to display in a stream (just like Facebook, Twitter, etc.)
It recently became necessary to allow the user to deeply search his stream; MongoDB's search was insufficient, so I implemented ElasticSearch on my servers (Amazon EC2 m1.large instances running CentOS, FWIW).
My question: I'm now in a position that I'm duplicating the data between MongoDB (where the user's stream is cached) and ElasticSearch (where it is searched).
Is there any disadvantage to moving my cache ENTIRELY into ElasticSearch, getting rid of the MongoDB all together? It seems a waste to double the storage, and there's no other place that I'm accessing this data (it is only used when presenting/searching the stream of posts).
Specifically, I want to make sure I'm not overlooking anything re: performance. I like the idea of reducing MongoDB as a bottleneck, yet I worry about the memory overhead of ElasticSearch. MongoDB runs on its own server in my cloud setup, whereas ElasticSearch is running on the same instances as node.js. This means I would have MORE ElasticSearch servers (the node.js servers are in an auto-scaling array), but they each are not DEDICATED servers (unlike MongoDB).
The only big obstacle to using ES as a "primary datasource" is that there isn't a good backup mechanism right now. The ES team is working on it and expect it to be out by the end of the year, but in the mean time, you'll have to implement your own backup scripts.
As far as performance, it's really hard to say because almost every situation is unique. ES benefits from memory - so more is always better. In particular, sorts/filters/facets/geo all like to eat memory. If you aren't doing much in the way of faceting, for example, you may be fine with less memory.
ES doesn't need to run on a dedicated node...but it will happily use as many resources as you give it.
Another option is to use just the elastic search indexes. You can choose to not save data in a readable format, so you search in ES and then retrieve documents from MongoDB to your user as needed.
The question bellow comments exactly on that.
Storing only selected fields and not storing _all in pyes/elasticsearch
I have an application that runs on Ubuntu Linux 12.04 which needs to store and retrieve a large number of large serialized objects. Currently the store is implemented by simply saving the serialized streams as files, where the filenames equal the md5 hash of the serialized object. However I would like to speed things up replacing the file-store by one that does in-memory caching of objects that are recently read/written, and preferably does the hashing for me.
The design of my application should not get any more complicated. Hence preferably would be a storing back-end that manages a key-value database and caching in an abstracted and efficient way. I am a bit lost with all of the key/value stores that are out there, and much of the topics/information seems to be outdated. I was initially looking at something like memcached+membase, but maybe there are better solutions out there. I looked into redis, mongodb, couchdb, but it is not quite clear to me if they fit my needs.
My most important requirements:
Transparent saving to a persistent store in a way that the most recently written/read objects are quickly available by automatically caching them in memory.
Store should survive a reboot. Hence in memory objects should be saved on disk asap.
Currently I am calculating the md5 manually. It would actually be nicer if the back-end does this for me. Hence the ability to get the hash-key when an object is stored, and be able to retrieve the object later using the hashkey.
Big plus is that if there are packages available for Ubuntu 12.04, either in universe or through launchpad or whatever.
Other than this, the software should preferably be light not be more complicated than necessary (I don't need distributed map-reduce jobs, etc)
Thanks for any advice!
I would normally suggest Redis because it will be fast and in-memory with asynch persistant store. Plus you'll find you can use their different data types for other purposes so not as single-purpose as memcached. As far as auto-hashing, I don't think it does that as you define your own keys when you store objects (as in most of them).
One downside to Redis is if you're storing a TON of binary objects, you'll be limited to available memory in RAM (unless sharding) so could reach performance limitations. In that case you may store objects on file system, hash them, and store keys in Redis and match that to filename stored on file server and you'd be fine.
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An alternate option would be to check out ElasticSearch which is like Mongo in that it stores objects native as JSON, but it includes the Lucene search engine on top with RESTful API interface. It "warms up" data in memory for fast response, but is also a persistent store and the nicest part is it auto-shards and auto-clusters using multicast to find other nodes.
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Hope that helps and if so, share the love! ;-)
I'd look at MongoDB. It caches things efficiently using your OS to page data in and out, and is pretty simple to setup. Redis and Memcached won't be good solutions for you because they keep everything in RAM. Other, simpler solutions like LevelDB or BDB would also probably be suitable. I don't think any database going to compute hashes automatically for you. It sounds like you already have code for this though.
I am working on a project were we are batch loading and storing huge volume of data in Oracle database which is constantly getting queried via Hibernate against this 100+ million records table (the reads are much more frequent than writes).
To speed things up we are using Lucene for some of queries (especially geo bounding box queries) and Hibernate second level cache but thats still not enough. We still have bottleneck in Hibernate queries against Oracle (we dont cache 100+ million table entities in Hibernate second level cache due to lack of that much memory).
What additional NoSQL solutions (apart from Lucene) I can leverage in this situation?
Some options I am thinking of are:
Use distributed ehcache (Terracotta) for Hibernate second level to leverage more memory across machines and reduce duplicate caches (right now each VM has its own cache).
To completely use in memory SQL database like H2 but unfortunately those solutions require loading 100+ mln tables into single VM.
Use Lucene for querying and BigTable (or distributed hashmap) for entity lookup by id.
What BigTable implementation will be suitable for this? I was considering HBase.
Use MongoDB for storing data and for querying and lookup by id.
recommending Cassandra with ElasticSearch for a scalable system (100 million is nothing for them). Use cassandra for all your data and ES for ad hoc and geo queries. Then you can kill your entire legacy stack. You may need a MQ system like rabbitmq for data sync between Cass. and ES.
It really depends on your data sets. The number one rule to NoSQL design is to define your query scenarios first. Once you really understand how you want to query the data then you can look into the various NoSQL solutions out there. The default unit of distribution is key. Therefore you need to remember that you need to be able to split your data between your node machines effectively otherwise you will end up with a horizontally scalable system with all the work still being done on one node (albeit better queries depending on the case).
You also need to think back to CAP theorem, most NoSQL databases are eventually consistent (CP or AP) while traditional Relational DBMS are CA. This will impact the way you handle data and creation of certain things, for example key generation can be come trickery.
Also remember than in some systems such as HBase there is no indexing concept. All your indexes will need to be built by your application logic and any updates and deletes will need to be managed as such. With Mongo you can actually create indexes on fields and query them relatively quickly, there is also the possibility to integrate Solr with Mongo. You don’t just need to query by ID in Mongo like you do in HBase which is a column family (aka Google BigTable style database) where you essentially have nested key-value pairs.
So once again it comes to your data, what you want to store, how you plan to store it, and most importantly how you want to access it. The Lily project looks very promising. THe work I am involved with we take a large amount of data from the web and we store it, analyse it, strip it down, parse it, analyse it, stream it, update it etc etc. We dont just use one system but many which are best suited to the job at hand. For this process we use different systems at different stages as it gives us fast access where we need it, provides the ability to stream and analyse data in real-time and importantly, keep track of everything as we go (as data loss in a prod system is a big deal) . I am using Hadoop, HBase, Hive, MongoDB, Solr, MySQL and even good old text files. Remember that to productionize a system using these technogies is a bit harder than installing Oracle on a server, some releases are not as stable and you really need to do your testing first. At the end of the day it really depends on the level of business resistance and the mission-critical nature of your system.
Another path that no one thus far has mentioned is NewSQL - i.e. Horizontally scalable RDBMSs... There are a few out there like MySQL cluster (i think) and VoltDB which may suit your cause.
Again it comes to understanding your data and the access patterns, NoSQL systems are also Non-Rel i.e. non-relational and are there for better suit to non-relational data sets. If your data is inherently relational and you need some SQL query features that really need to do things like Cartesian products (aka joins) then you may well be better of sticking with Oracle and investing some time in indexing, sharding and performance tuning.
My advice would be to actually play around with a few different systems. Look at;
MongoDB - Document - CP
CouchDB - Document - AP
Redis - In memory key-value (not column family) - CP
Cassandra - Column Family - Available & Partition Tolerant (AP)
HBase - Column Family - Consistent & Partition Tolerant (CP)
Hadoop/Hive
VoltDB - A really good looking product, a relation database that is distributed and might work for your case (may be an easier move). They also seem to provide enterprise support which may be more suited for a prod env (i.e. give business users a sense of security).
Any way thats my 2c. Playing around with the systems is really the only way your going to find out what really works for your case.
As you suggest MongoDB (or any similar NoSQL persistence solution) is an appropriate fit for you. We've run tests with significantly larger data sets than the one you're suggesting on MongoDB and it works fine. Especially if you're read heavy MongoDB's sharding and/or distributing reads across replicate set members will allow you to speed up your queries significantly. If your usecase allows for keeping your indexes right balanced your goal of getting close to 20ms queries should become feasable without further caching.
You should also check out the Lily project (lilyproject.org). They have integrated HBase with Solr. Internally they use message queues to keep Solr in sync with HBase. This allows them to have the speed of solr indexing (sharding and replication), backed by a highly reliable data storage system.
you could group requests & split them specific to a set of data & have a single (or a group of servers) process that, here you can have the data available in the cache to improve performance.
e.g.,
say, employee & availability data are handled using 10 tables, these can be handled b a small group of server (s) when you configure hibernate cache to load & handle requests.
for this to work you need a load balancer (which balances load by business scenario).
not sure how much of it can be implemented here.
At the 100M records your bottleneck is likely Hibernate, not Oracle. Our customers routinely have billions of records in the individual fact tables of our Oracle-based data warehouse and it handles them fine.
What kind of queries do you execute on your table?
I'm building a system that tracks and verifies ad impressions and clicks. This means that there are a lot of insert commands (about 90/second average, peaking at 250) and some read operations, but the focus is on performance and making it blazing-fast.
The system is currently on MongoDB, but I've been introduced to Cassandra and Redis since then. Would it be a good idea to go to one of these two solutions, rather than stay on MongoDB? Why or why not?
Thank you
For a harvesting solution like this, I would recommend a multi-stage approach. Redis is good at real time communication. Redis is designed as an in-memory key/value store and inherits some very nice benefits of being a memory database: O(1) list operations. For as long as there is RAM to use on a server, Redis will not slow down pushing to the end of your lists which is good when you need to insert items at such an extreme rate. Unfortunately, Redis can't operate with data sets larger than the amount of RAM you have (it only writes to disk, reading is for restarting the server or in case of a system crash) and scaling has to be done by you and your application. (A common way is to spread keys across numerous servers, which is implemented by some Redis drivers especially those for Ruby on Rails.) Redis also has support for simple publish/subscribe messenging, which can be useful at times as well.
In this scenario, Redis is "stage one." For each specific type of event you create a list in Redis with a unique name; for example we have "page viewed" and "link clicked." For simplicity we want to make sure the data in each list is the same structure; link clicked may have a user token, link name and URL, while the page viewed may only have the user token and URL. Your first concern is just getting the fact it happened and whatever absolutely neccesary data you need is pushed.
Next we have some simple processing workers that take this frantically inserted information off of Redis' hands, by asking it to take an item off the end of the list and hand it over. The worker can make any adjustments/deduplication/ID lookups needed to properly file the data and hand it off to a more permanent storage site. Fire up as many of these workers as you need to keep Redis' memory load bearable. You could write the workers in anything you wish (Node.js, C#, Java, ...) as long as it has a Redis driver (most web languages do now) and one for your desired storage (SQL, Mongo, etc.)
MongoDB is good at document storage. Unlike Redis it is able to deal with databases larger than RAM and it supports sharding/replication on it's own. An advantage of MongoDB over SQL-based options is that you don't have to have a predetermined schema, you're free to change the way data is stored however you want at any time.
I would, however, suggest Redis or Mongo for the "step one" phase of holding data for processing and use a traditional SQL setup (Postgres or MSSQL, perhaps) to store post-processed data. Tracking client behavior sounds like relational data to me, since you may want to go "Show me everyone who views this page" or "How many pages did this person view on this given day" or "What day had the most viewers in total?". There may be even more complex joins or queries for analytic purposes you come up with, and mature SQL solutions can do a lot of this filtering for you; NoSQL (Mongo or Redis specifically) can't do joins or complex queries across varied sets of data.
I currently work for a very large ad network and we write to flat files :)
I'm personally a Mongo fan, but frankly, Redis and Cassandra are unlikely to perform either better or worse. I mean, all you're doing is throwing stuff into memory and then flushing to disk in the background (both Mongo and Redis do this).
If you're looking for blazing fast speed, the other option is to keep several impressions in local memory and then flush them disk every minute or so. Of course, this is basically what Mongo and Redis do for you. Not a real compelling reason to move.
All three solutions (four if you count flat-files) will give you blazing fast writes. The non-relational (nosql) solutions will give you tunable fault-tolerance as well for the purposes of disaster recovery.
In terms of scale, our test environment, with only three MongoDB nodes, can handle 2-3k mixed transactions per second. At 8 nodes, we can handle 12k-15k mixed transactions per second. Cassandra can scale even higher. 250 reads is (or should be) no problem.
The more important question is, what do you want to do with this data? Operational reporting? Time-series analysis? Ad-hoc pattern analysis? real-time reporting?
MongoDB is a good option if you want the ability to do ad-hoc analysis based on multiple attributes within a collection. You can put up to 40 indexes on a collection, though the indexes will be stored in-memory, so watch for size. But the result is a flexible analytical solution.
Cassandra is a key-value store. You define a static column or set of columns that will act as your primary index right up front. All queries run against Cassandra should be tuned to this index. You can put a secondary on it, but that's about as far as it goes. You can, of course, use MapReduce to scan the store for non-key attribution, but it will be just that: a serial scan through the store. Cassandra also doesn't have the notion of "like" or regex operations on the server nodes. If you want to find all customers where the first name starts with "Alex", you'll have to scan through the entire collection, pull the first name out for each entry and run it through a client-side regex.
I'm not familiar enough with Redis to speak intelligently about it. Sorry.
If you are evaluating non-relational platforms, you might also want to consider CouchDB and Riak.
Hope this helps.
Just found this: http://blog.axant.it/archives/236
Quoting the most interesting part:
This second graph is about Redis RPUSH vs Mongo $PUSH vs Mongo insert, and I find this graph to be really interesting. Up to 5000 entries mongodb $push is faster even when compared to Redis RPUSH, then it becames incredibly slow, probably the mongodb array type has linear insertion time and so it becomes slower and slower. mongodb might gain a bit of performances by exposing a constant time insertion list type, but even with the linear time array type (which can guarantee constant time look-up) it has its applications for small sets of data.
I guess everything depends at least on data type and volume. Best advice probably would be to benchmark on your typical dataset and see yourself.
According to the Benchmarking Top NoSQL Databases (download here)
I recommend Cassandra.
If you have the choice (and need to move away from flat fies) I would go with Redis. Its blazingly fast, will comfortably handle the load you're talking about, but more importantly you won't have to manage the flushing/IO code. I understand its pretty straight forward but less code to manage is better than more.
You will also get horizontal scaling options with Redis that you may not get with file based caching.
I can get around 30k inserts/sec with MongoDB on a simple $350 Dell. If you only need around 2k inserts/sec, I would stick with MongoDB and shard it for scalability. Maybe also look into doing something with Node.js or something similar to make things more asynchronous.
The problem with inserts into databases is that they usually require writing to a random block on disk for each insert. What you want is something that only writes to disk every 10 inserts or so, ideally to sequential blocks.
Flat files are good. Summary statistics (eg total hits per page) can be obtained from flat files in a scalable manner using merge-sorty map-reducy type algorithms. It's not too hard to roll your own.
SQLite now supports Write Ahead Logging, which may also provide adequate performance.
I have hand-on experience with mongodb, couchdb and cassandra. I converted a lot of files to base64 string and insert these string into nosql.
mongodb is the fastest. cassandra is slowest. couchdb is slow too.
I think mysql would be much faster than all of them, but I didn't try mysql for my test case yet.