I am currently using mysql. I am finding that my schema is getting incredibly complicated. I seek to find a new db that will suit my needs:
Let's assume I am building a news aggregrator (which collects news from multiple website). I then run algorithms to determine if two news from different sites are actually referring to the same topic. I run this algorithm to cluster news together. The relationship is depicted below:
cluster
\--news1
\--word1
\--word2
\--news2
\--word3
\--news3
\--word1
\--word3
And then I will apply some magic and determine the importance of each word. Summing all the importance of each word gives me the importance of a news article. Summing the importance of each news article gives me the importance of a cluster.
Note that above cluster there are also subgroups( like split by region etc), and categories (like sports, etc) which I have to determine the importance of that in a particular day per se.
I have used views in the past to do so, but I realized that views are very slow. So i will normally do an insert into an actual table and index them for better performance. As you can see this leads to multiple tables derived like (cluster, importance), (news, importance), (words, importance) etc which can get pretty messy.
Also the "importance" metric will change. It has become increasingly difficult to alter tables, update data (which I am using TRUNCATE TABLE) and then inserting from null.
I am currently looking into something schemaless like Mongodb. I do not need distributedness. I would very much want something that is reasonably fast (which can be indexed) and something that is a lot more flexible that traditional RDMBS.
NEW
As requested by various people, I will post my usage to this database (they are not actual SQL queries since I hope everyone here could understand)
TABLE word ( word_id, news_id, word )
TABLE news ( news_id, date, site .. )
TABLE clusters ( cluster_id, cluster_leader, cluster_name, ... )
TABLE mapping_clusters_news( cluster_id, news_id)
TABLE word_importance (word_id, score)
TABLE news_importance (news_id, score)
TABLE cluster_importance( cluster_id, score)
TABLE group_importance( cluster_id, score)
You might notice that TABLE_word has an extra news_id column. This is to correspond to TABLE_word_importance column because the same word can have different importance in different articles (if you are familiar with tfidf, this is basically something like that).
All the "importance" table now calculates the importance of each entity by averaging the importance of all the sub-entities below it. This means that Each cluster's importance is determined by all the news inside it, each news's importance is determined by all the words inside it etc.
TYPICAL USAGE:
1) SELECT clusters FROM db THAT HAS word1, word2, word3, .. ORDER BY cluster_importance_score
2) SELECT words FROM db BELONGING TO THE CLUSTER cluster_id=5 ODER BY word_importance score.
3) SELECT groups ordered by importance score.
As you can see, I am deriving a lot of scores from each layer, and someone have been telling me to use a materialized view for this purpose (which postgresql supports it). However, as you can see, this simple schema already consists of 8 tables (my actual db consists of 26 tables of crap like that, which is adding so much additional layers of complexity for maintainance).
NOTE THIS IS NOT ABOUT FULL-TEXT SEARCH.
When the schema is getting complicated, a graph database can be a good alternative. As I understand your domain, you have lots of entities related to other entities in different ways. Would it make sense to you to model this as a graph/network of entities? As food for thought I whipped up an example using Neo4j:
news-analysis-example http://github.com/neo4j-examples/domain-models/raw/master/news-analysis.png
In a graphdb you can set properties on both nodes and relationships, which could be useful in your case (for instance the number of times a word is used in a news entry could be added to the relationship to that word). BTW, I added an extra is_related relationship between two news items, as I thought that could be interesting as well.
How about db4o? db4o
ORM means "Object-relational mapper". Not using a relational database wouldn't make much sense. I'll pretend you meant "I want to be able to serialize objects".
I don't understand why distributedness is not required. Could you elaborate on that?
Personally, I would reccomend Cassandra. It still has reasonably close ties to (by which I mean easy to integrate with) Hadoop, which you will probably eventually want for your processing. As an added bonus, there's Telephus, so Cassandra supports Twisted beautifully. Cassandra's method of conflict resolution (currently timestamps, soon-ish vector clocks) might work for your changing metric as long as you don't mind getting the old value for as long as the metric hasn't been recalculated. Otherwise, you might move up a level and simply store multiple versions of the data with different versions of the metric. That way, if you decide a metric is a bad idea, you don't have to recompute.
Cassandra, unfortunately, does not have something that serializes/deserializes objects very well yet. However, for the thin wrappers you would be writing (essentially structs with a few methods), would writing a fromCassandra #classmethod really be that big a deal?
Postgresql may be "schema based" but it kind of feels like you're throwing the baby out with the bathwater. If you don't need a distributed db or a particularly schema-less design (which it doesn't sound like offhand you do, but you appear to think you do) then I'm not sure why you would want mongodb. Postgres has lots of indexing options and it sounds like its built in full text searching would be good for you. If you're used to MySQL and altering tables (you mentioned issues there) can be a nightmare, mostly its better in Postgres. I'm a fan on Postgres and MongoDB - it just don't sound like there's a good reason to move away from a relational db for data that certainly sounds relational in nature.
In a word, YES, you should probably be looking at something else: Cassandra, Hadoop, MongoDB, something.
MongoDB is basically going to reduce your sample schema to "clusters" and "news", with everything else basically being contained in those two.
The good news:
This will make it easy to modify fields.
Map-reduce operations are a natural fit for the type of work that you're doing. You perform a map-reduce and then save the data back to the "news" item and all will be well.
The bad news:
It's easy to lose track of the structure of data with something like Mongo. Hadoop and Hive typically force your schema little more. But in any case, you'll need to write down some form of schema or just drown.
If you plan to do this for some non-trivial amount of data, then you're going to want "horizontal" scalability. MongoDB is "ok" for this, Hadoop is definitely a "leader" for this.
Related
We currently have a table which stores information about users. Some of the columns hold information such as user ID, name etc., but many other columns (booleans, integers and varchars etc) hold configuration options for each user.
This has over time resulted in the width of the table becoming quite big and I think the time has come to migrate this to something new, so I want to remove all the "option"-related columns to a separate data structure.
The typical way of doing this, from my experience, would be to have a new table which would simply have option_id and option_name, and a second new table which would contain user_id, option_id, option_value, for example.
However, a colleague suggested using the new jsonb column type as an alternative, but I don't know if I like the idea of storing relational data in a non-relational way. From a Java point of view, it's pretty much the same as far as I can tell - it'll just be turned into a POJO and then cached on the object.
I should mention the number of users will be quite low, only going into the thousands, and number of columns could and will go into the hundreds.
Does anyone have advice on the best way forward here?
Technically, you have already de-normalized your database structure by adding columns to a table that are irrelevant to some of the entities stored therein.
Using JSON is just another way to de-normalize, cramming a bunch of values into a single row-column field. The excellent binary support for JSON in Postgres (the jsonb data type) then lets you index elements within those JSON documents, as a way to quickly access those embedded values. This is quite screwy from a relational point of view, but is handy for some situations.
Either approach is commonly done for this kind of problem, and is not necessarily bad. In general, de-normalizing is often a pay-now-or-pay-later kind of solution. But for something like user preferences, there may not be a pay-later penalty, as there often is with most business-oriented problem domains.
Nevertheless, you should consider a normalized database structure.
By the way, this kind of table-structure Question might be better asked in the sister site, http://DBA.StackExchange.com/.
I suggest searching Stack Overflow, that DBA site, and the wider Internet for discussions of database design for storing user preferences. Like this.
I am trying to understand Cassandra and how to structure my column families (CF) but it's quite hard since I am used to relational databases.
For example if I create simple users CF and I try to insert new row, how can I make an incremental key like in MySQL?
I saw a lot of examples where you would just put the username instead of unique ID and that would make a little sense, but what if I want users to have duplicated usernames?
Also how can I make searches when from what I understand cassandra does not suport > operators, so something like select * from users where something > something2 would not work.
And probably the most important question what about grouping? Would I need to retrieve all data and then filter it with whatever language I am using? I think that would slow down my system a lot.
So basically I need some brief explanation how to get started with Cassanda.
Your questions are quite general, but let me take a stab at it. First, you need to model your data in terms of your queries. With an RDBMS, you model your data in some normalized form, then optimize later for your specific queries. You cannot do this with Cassandra; you must write your data the way you intend to read it. Often this means writing it more than one way. In general, it helps to completely shed your RDBMS thinking if you want to work effectively with Cassandra.
Regarding keys:
They are used in Cassandra as the unit of distribution across the ring. So your key will get hashed and assigned an "owner" in the ring. Use the RandomPartitioner to guarantee even distribution
Presuming you use RandomPartitioner (you should), keys are not sorted. This means you cannot ask for a range of keys. You can, however, ask for a list of keys in a single query.
Keys are relevant in some models and not in others. If your model requires query-by-key, you can use any unique value that your application is aware of (such as a UUID). Sometimes keys are sentinel values, such as a Unix epoch representing the start of the day. This allows you to hand Cassandra a bunch of known keys, then get a range of data sorted by column (see below).
Regarding query predicates:
You can get ranges of data presuming you model it correctly to answer your queries.
Since columns are written in sorted order, you can query a range from column A to column n with a slice query (which is very fast). You can also use composite columns to abstract this mechanism a bit.
You can use secondary indexes on columns where you have low cardinality--this gives you query-by-value functionality.
You can create your own indexes where the data is sorted the way you need it.
Regarding grouping:
I presume you're referring to creating aggregates. If you need your data in real-time, you'll want to use some external mechanism (like Storm) to track data and constantly update your relevant aggregates into a CF. If you are creating aggregates as part of a batch process, Cassandra has excellent integration with Hadoop, allowing you to write map/reduce jobs in Pig, Hive, or directly in your language of choice.
To your first question:
can i make incremental key like in mysql
No, not really -- not native to Cassandra. How to create auto increment IDs in Cassandra -- You could check here for more information: http://srinathsview.blogspot.ch/2012/04/generating-distributed-sequence-number.html
Your second question is more about how you store and model your Cassandra data.
Check out stackoverflow's search option. Lots of interesting questions!
Switching from MySQL to Cassandra - Pros/Cons?
Cassandra Data Model
Cassandra/NoSQL newbie: the right way to model?
Apache Cassandra schema design
Knowledge sources for Apache Cassandra
Most importantly, When NOT to use Cassandra?
You may want to check out PlayOrm. While I agree you need to break out of RDBMS thinking sometimes having your primary key as userid is just the wrong choice. Sometimes it is the right choice(depends on your requirements).
PlayOrm is a mix of noSQL and relational concepts as you need both and you can do Scalable-SQL with joins and everything. You just need to partition the tables you believe will grow into the billions/trillions of rows and you can query into those partitions. Even with CQL, you need to partition your tables. What can you partition by? time is good for some use-cases. Others can be partitioned by clients as each client is really a mini-database in your noSQL cluster.
As far as keys go, PlayOrm generates unique "cluster" keys which is hostname-uniqueidinThatHost, basically like a TimeUUID except quite a bit shorter and more readable as we use hostnames in our cluster of a1, a2, a3, etc. etc.
I currently run a MySQL-powered website where users promote advertisements and gain revenue every time someone completes one. We log every time someone views an ad ("impression"), every time a user clicks an add ("click"), and every time someone completes an ad ("lead").
Since we get so much traffic, we have millions of records in each of these respective tables. We then have to query these tables to let users see how much they have earned, so we end up performing multiple queries on tables with millions and millions of rows multiple times in one request, hundreds of times concurrently.
We're looking to move away from MySQL and to a key-value store or something along those lines. We need something that will let us store all these millions of rows, query them in milliseconds, and MOST IMPORTANTLY, use adhoc queries where we can query any single column, so we could do things like:
FROM leads WHERE country = 'US' AND user_id = 501 (the NoSQL equivalent, obviously)
FROM clicks WHERE ad_id = 1952 AND user_id = 200 AND country = 'GB'
etc.
Does anyone have any good suggestions? I was considering MongoDB or CouchDB but I'm not sure if they can handle querying millions of records multiple times a second and the type of adhoc queries we need.
Thanks!
With those requirements, you are probably better off sticking with SQL and setting up replication/clustering if you are running into load issues. You can set up indexing on a document database so that those queries are possible, but you don't really gain anything over your current system.
NoSQL systems generally improve performance by leaving out some of the more complex features of relational systems. This means that they will only help if your scenario doesn't require those features. Running ad hoc queries on tabular data is exactly what SQL was designed for.
CouchDB's map/reduce is incremental which means it only processes a document once and stores the results.
Let's assume, for a moment, that CouchDB is the slowest database in the world. Your first query with millions of rows takes, maybe, 20 hours. That sounds terrible. However, your second query, your third query, your fourth query, and your hundredth query will take 50 milliseconds, perhaps 100 including HTTP and network latency.
You could say CouchDB fails the benchmarks but gets honors in the school of hard knocks.
I would not worry about performance, but rather if CouchDB can satisfy your ad-hoc query requirements. CouchDB wants to know what queries will occur, so it can do the hard work up-front before the query arrives. When the query does arrive, the answer is already prepared and out it goes!
All of your examples are possible with CouchDB. A so-called merge-join (lots of equality conditions) is no problem. However CouchDB cannot support multiple inequality queries simultaneously. You cannot ask CouchDB, in a single query, for users between age 18-40 who also clicked fewer than 10 times.
The nice thing about CouchDB's HTTP and Javascript interface is, it's easy to do a quick feasibility study. I suggest you try it out!
Most people would probably recommend MongoDB for a tracking/analytic system like this, for good reasons. You should read the „MongoDB for Real-Time Analytics” chapter from the „MongoDB Definitive Guide” book. Depending on the size of your data and scaling needs, you could get all the performance, schema-free storage and ad-hoc querying features. You will need to decide for yourself if issues with durability and unpredictability of the system are risky for you or not.
For a simpler tracking system, Redis would be a very good choice, offering rich functionality, blazing speed and real durability. To get a feel how such a system would be implemented in Redis, see this gist. The downside is, that you'd need to define all the „indices” by yourself, not gain them for „free”, as is the case with MongoDB. Nevertheless, there's no free lunch, and MongoDB indices are definitely not a free lunch.
I think you should have a look into how ElasticSearch would enable you:
Blazing speed
Schema-free storage
Sharding and distributed architecture
Powerful analytic primitives in the form of facets
Easy implementation of „sliding window”-type of data storage with index aliases
It is in heart a „fulltext search engine”, but don't get yourself confused by that. Read the „Data Visualization with ElasticSearch and Protovis“ article for real world use case of ElasticSearch as a data mining engine.
Have a look on these slides for real world use case for „sliding window” scenario.
There are many client libraries for ElasticSearch available, such as Tire for Ruby, so it's easy to get off the ground with a prototype quickly.
For the record (with all due respect to #jhs :), based on my experience, I cannot imagine an implementation where Couchdb is a feasible and useful option. It would be an awesome backup storage for your data, though.
If your working set can fit in the memory, and you index the right fields in the document, you'd be all set. Your ask is not something very typical and I am sure with proper hardware, right collection design (denormalize!) and indexing you should be good to go. Read up on Mongo querying, and use explain() to test the queries. Stay away from IN and NOT IN clauses that'd be my suggestion.
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 MySQL 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. However for your use case I think a Column Family database may be the best solution, I think there are a few places which have implemented similar solutions to very similar problems (I think the NYTimes is using HBase to monitor user page clicks). Another great example is Facebook and like, they are using HBase for this. There is a really good article here which may help you along your way and further explain some points above. http://highscalability.com/blog/2011/3/22/facebooks-new-realtime-analytics-system-hbase-to-process-20.html
Final point would be that NoSQL systems are not the be all and end all. Putting your data into a NoSQL database does not mean its going to perform any better than MySQL, Oracle or even text files... For example see this blog post: http://mysqldba.blogspot.com/2010/03/cassandra-is-my-nosql-solution-but.html
I'd have a 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 - Also have a look at Hadoop streaming...
Hypertable - Another CF CP DB.
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.
From what I saw in this video...
http://www.youtube.com/watch?v=LhnGarRsKnA
pretty much all the traditional RDBM operations (JOINS, GROUP BY, HAVING, etc) can be done in NoSQL databases through a combination of MapReduce/denormalization techniques.
Is there any article/document that has all these equivalences clearly described. Something like... the equivalence of a JOIN in a NoSQL database would be... bla bla
I just can't find this kind of documentation anywhere :(
There's a reason you can't find that type of documentation. For a start, unlike SQL, there is no such thing as a standard NoSQL database. Have you tried searching for specific NoSQL data stores?
Also, trying to convert relational operations to non relational systems will just get you into trouble. Instead you need to look at what you are trying to do with those relational operations. For example is group by for sorting a list with categories or for handling heirarchical objects when you don't have multiple value fields? Is join assembling a single object stored in multiple tables or calculating a set intersection?
One of the biggest strengths of SQL is that any data can be represented in a standard way and any query can be run on that data. It won't necessarily be the best fit for that data, but it is standard and there is a single correct answer for almost any question. NoSQL is mostly about being able to optimize your data store for what you actually need by sacrificing the generality of SQL. That may be performance, handling a large dataset, handling inconsistent data, or just simpler code. In short you need to understand your requirements and the tradeoffs involved in optimizing for them rather than just choosing SQL by default.
Your best option is to pick a datastore that fits what you need (a good comparison of high level features is at
http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis ) and look for some examples of how systems are designed using that data store. With luck you will find something close to what you are working on.
This article has a some good info on NoSQL vs using SQL
http://www.develop.com/mongoDB
Be very careful... you probably don't want to join a 1 trillion row table with something. Think about patterns of partitioning your data. For example with playOrm you can partition your data and do S-SQL(Scalable SQL) so you can do all the joins you want to within a partition which in many OLTP apps is exactly what you need as if your customers are businesses, each business is in it's own partition and you can join all you want all the tables related to that one business.
Here is a list of patterns to help you gain ground in the noSql world....(it's a work in progress).
https://github.com/deanhiller/playorm/wiki/Patterns-Page
Can we get a list of basic optimization techniques going (anything from modeling to querying, creating indexes, views to query optimization). It would be nice to have a list of these, one technique per answer. As a hobbyist I would find this to be very useful, thanks.
And for the sake of not being too vague, let's say we are using a maintstream DB such as MySQL or Oracle, and that the DB will contain 500,000-1m or so records across ~10 tables, some with foreign key contraints, all using the most typical storage engines (eg: InnoDB for MySQL). And of course, the basics such as PKs are defined as well as FK contraints.
Learn about indexes, and use them properly. Generally speaking*, follow these guidelines:
Every table should have a clustered index
Fields used for filters and sorts are good candidates for indexing
More selective fields are better candidates for indexing
For best performance on crucial queries, design "covering indexes" for those queries
Make sure your indexes are actually being used, and remove those that aren't
If your table has 15 fields, and you make 15 indexes, each with only a single field, you're doing it wrong :)
*There are some exceptions to these rules if you know what you're doing. My experience is Microsoft SQL Server, but I would presume most of this advice would still apply to a different RDMS.
IMO, by far the best optimization is to have the data model fit the problem domain for which it was built. When it does not, the resulting symptom is difficult-to-write or convoluted queries in order to get the information desired and that typically rears itself when reports are built against the database. Thus, in designing a database it helps to have an idea as to the types and nature of the information, such as reports, that the users will want from the system.
When talking database design, check out the database normalization, e.g. the wikipedia article: Normal forms.
If you have a good design and still you need to optimize for performance, try Denormalisation.
If you have specific needs which are not covered by relational model efficiently, look at other models covered by the term NoSQL.
Some query/schema optimizations:
Be mindful when using DISTINCT or GROUP BY. I find that many new developers will use DISTINCT in places where it really is not needed or could be rewritten more efficiently using an Exists statement or a derived query.
Be mindful of Left Joins. All too often I find new SQL developers will ignore the schema in place and use Left Joins where they really are not necessary. For example:
Select
From Orders
Left Join Customers
On Customers.Id = Orders.CustomerId
If Orders.CustomerId is a required column, then it is not necessary to use a left join.
Be a student of new features. Currently, MySQL does not support common-table expressions which means that some types of queries are cumbersome and probably slower to write than they would be if CTEs were supported. However, that will not be true forever. Keep up on new syntax features in MySQL which might be used to make existing queries more efficient.
You do not have to use surrogate keys everywhere. There might be tables better suited to an intelligent key (e.g. US State abbreviations, Currency Codes etc) which would enable developers to avoid additional joins in many cases.
If possible, find ways of archiving data to an OLAP or reporting server. The smaller you can make the production data, the faster it will run.
A design that concisely models your problem is always a good start. Overgeneralizing the data model can lead to performance problems. For example, I've heard reports of projects striving for uber-flexibility that use the RDBMS as a dumb "name/value" store - and resulting performance was appalling.
Once a good design is in place, then use the tools provided by the RDBMS to help it achieve good performance. Single field PKs (no composites), but composite business keys as an index with unique constraint, use of appropriate data types, e.g. using appropriate numeric types for numeric values rather than char or similar. Physical attributes of the hardware the RDBMS is running on should also be considered, since the bulk of query time is often disk I/O - but of course don't take this for granted - use a profiler to find out where the time is going.
Depending upon the update/query ratio, materialized views/indexed views can be useful in improving performance for slow running queries. A poor-man's alternative is to use triggers to invoke a procedure that populates the table with a result of a slow-running, infrequently-changed view.
Query optimization is a bit of a black art since it is often database-dependent, but some rules of thumb are given here - Optimizing SQL.
Finally, although possibly outside the intended scope of your question, use a good data access layer in your application, and avoid the temptation to roll your own - there are surely tested and performant implementations available for all major languages. Use of caching at the data access layer, middle tier and application layer can help improve performance considerably.
Do use less query whenever possible. Use "JOIN", and group your tables so that a single query gives your results.
A good example is the Modified Preorder Tree Transversal (MPTT) to get all of a tree node parents, ordered, in a single query.
Take a holistic approach to optimization.
Consider the impact of slow disks, network latency, lack of memory, and server load.