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I've been hearing things about NoSQL and that it may eventually become the replacement for SQL DB storage methods due to the fact that DB interaction is often a bottle neck for speed on the web.
So I just have a few questions:
What exactly is it?
How does it work?
Why would it be better than using a SQL Database? And how much better is it?
Is the technology too new to start implementing yet or is it worth taking a look into?
There is no such thing as NoSQL!
NoSQL is a buzzword.
For decades, when people were talking about databases, they meant relational databases. And when people were talking about relational databases, they meant those you control with Edgar F. Codd's Structured Query Language. Storing data in some other way? Madness! Anything else is just flatfiles.
But in the past few years, people started to question this dogma. People wondered if tables with rows and columns are really the only way to represent data. People started thinking and coding, and came up with many new concepts how data could be organized. And they started to create new database systems designed for these new ways of working with data.
The philosophies of all these databases were different. But one thing all these databases had in common, was that the Structured Query Language was no longer a good fit for using them. So each database replaced SQL with their own query languages. And so the term NoSQL was born, as a label for all database technologies which defy the classic relational database model.
So what do NoSQL databases have in common?
Actually, not much.
You often hear phrases like:
NoSQL is scalable!
NoSQL is for BigData!
NoSQL violates ACID!
NoSQL is a glorified key/value store!
Is that true? Well, some of these statements might be true for some databases commonly called NoSQL, but every single one is also false for at least one other. Actually, the only thing NoSQL databases have in common, is that they are databases which do not use SQL. That's it. The only thing that defines them is what sets them apart from each other.
So what sets NoSQL databases apart?
So we made clear that all those databases commonly referred to as NoSQL are too different to evaluate them together. Each of them needs to be evaluated separately to decide if they are a good fit to solve a specific problem. But where do we begin? Thankfully, NoSQL databases can be grouped into certain categories, which are suitable for different use-cases:
Document-oriented
Examples: MongoDB, CouchDB
Strengths: Heterogenous data, working object-oriented, agile development
Their advantage is that they do not require a consistent data structure. They are useful when your requirements and thus your database layout changes constantly, or when you are dealing with datasets which belong together but still look very differently. When you have a lot of tables with two columns called "key" and "value", then these might be worth looking into.
Graph databases
Examples: Neo4j, GiraffeDB.
Strengths: Data Mining
While most NoSQL databases abandon the concept of managing data relations, these databases embrace it even more than those so-called relational databases.
Their focus is at defining data by its relation to other data. When you have a lot of tables with primary keys which are the primary keys of two other tables (and maybe some data describing the relation between them), then these might be something for you.
Key-Value Stores
Examples: Redis, Cassandra, MemcacheDB
Strengths: Fast lookup of values by known keys
They are very simplistic, but that makes them fast and easy to use. When you have no need for stored procedures, constraints, triggers and all those advanced database features and you just want fast storage and retrieval of your data, then those are for you.
Unfortunately they assume that you know exactly what you are looking for. You need the profile of User157641? No problem, will only take microseconds. But what when you want the names of all users who are aged between 16 and 24, have "waffles" as their favorite food and logged in in the last 24 hours? Tough luck. When you don't have a definite and unique key for a specific result, you can't get it out of your K-V store that easily.
Is SQL obsolete?
Some NoSQL proponents claim that their favorite NoSQL database is the new way of doing things, and SQL is a thing of the past.
Are they right?
No, of course they aren't. While there are problems SQL isn't suitable for, it still got its strengths. Lots of data models are simply best represented as a collection of tables which reference each other. Especially because most database programmers were trained for decades to think of data in a relational way, and trying to press this mindset onto a new technology which wasn't made for it rarely ends well.
NoSQL databases aren't a replacement for SQL - they are an alternative.
Most software ecosystems around the different NoSQL databases aren't as mature yet. While there are advances, you still haven't got supplemental tools which are as mature and powerful as those available for popular SQL databases.
Also, there is much more know-how for SQL around. Generations of computer scientists have spent decades of their careers into research focusing on relational databases, and it shows: The literature written about SQL databases and relational data modelling, both practical and theoretical, could fill multiple libraries full of books. How to build a relational database for your data is a topic so well-researched it's hard to find a corner case where there isn't a generally accepted by-the-book best practice.
Most NoSQL databases, on the other hand, are still in their infancy. We are still figuring out the best way to use them.
What exactly is it?
On one hand, a specific system, but it has also become a generic word for a variety of new data storage backends that do not follow the relational DB model.
How does it work?
Each of the systems labelled with the generic name works differently, but the basic idea is to offer better scalability and performance by using DB models that don't support all the functionality of a generic RDBMS, but still enough functionality to be useful. In a way it's like MySQL, which at one time lacked support for transactions but, exactly because of that, managed to outperform other DB systems. If you could write your app in a way that didn't require transactions, it was great.
Why would it be better than using a SQL Database? And how much better is it?
It would be better when your site needs to scale so massively that the best RDBMS running on the best hardware you can afford and optimized as much as possible simply can't keep up with the load. How much better it is depends on the specific use case (lots of update activity combined with lots of joins is very hard on "traditional" RDBMSs) - could well be a factor of 1000 in extreme cases.
Is the technology too new to start implementing yet or is it worth taking a look into?
Depends mainly on what you're trying to achieve. It's certainly mature enough to use. But few applications really need to scale that massively. For most, a traditional RDBMS is sufficient. However, with internet usage becoming more ubiquitous all the time, it's quite likely that applications that do will become more common (though probably not dominant).
Since someone said that my previous post was off-topic, I'll try to compensate :-) NoSQL is not, and never was, intended to be a replacement for more mainstream SQL databases, but a couple of words are in order to get things in the right perspective.
At the very heart of the NoSQL philosophy lies the consideration that, possibly for commercial and portability reasons, SQL engines tend to disregard the tremendous power of the UNIX operating system and its derivatives.
With a filesystem-based database, you can take immediate advantage of the ever-increasing capabilities and power of the underlying operating system, which have been steadily increasing for many years now in accordance with Moore's law. With this approach, many operating-system commands become automatically also "database operators" (think of "ls" "sort", "find" and the other countless UNIX shell utilities).
With this in mind, and a bit of creativity, you can indeed devise a filesystem-based database that is able to overcome the limitations of many common SQL engines, at least for specific usage patterns, which is the whole point behind NoSQL's philosophy, the way I see it.
I run hundreds of web sites and they all use NoSQL to a greater or lesser extent. In fact, they do not host huge amounts of data, but even if some of them did I could probably think of a creative use of NoSQL and the filesystem to overcome any bottlenecks. Something that would likely be more difficult with traditional SQL "jails". I urge you to google for "unix", "manis" and "shaffer" to understand what I mean.
If I recall correctly, it refers to types of databases that don't necessarily follow the relational form. Document databases come to mind, databases without a specific structure, and which don't use SQL as a specific query language.
It's generally better suited to web applications that rely on performance of the database, and don't need more advanced features of Relation Database Engines. For example, a Key->Value store providing a simple query by id interface might be 10-100x faster than the corresponding SQL server implementation, with a lower developer maintenance cost.
One example is this paper for an OLTP Tuple Store, which sacrificed transactions for single threaded processing (no concurrency problem because no concurrency allowed), and kept all data in memory; achieving 10-100x better performance as compared to a similar RDBMS driven system. Basically, it's moving away from the 'One Size Fits All' view of SQL and database systems.
In practice, NoSQL is a database system which supports fast access to large binary objects (docs, jpgs etc) using a key based access strategy. This is a departure from the traditional SQL access which is only good enough for alphanumeric values. Not only the internal storage and access strategy but also the syntax and limitations on the display format restricts the traditional SQL. BLOB implementations of traditional relational databases too suffer from these restrictions.
Behind the scene it is an indirect admission of the failure of the SQL model to support any form of OLTP or support for new dataformats. "Support" means not just store but full access capabilities - programmatic and querywise using the standard model.
Relational enthusiasts were quick to modify the defnition of NoSQL from Not-SQL to Not-Only-SQL to keep SQL still in the picture! This is not good especially when we see that most Java programs today resort to ORM mapping of the underlying relational model. A new concept must have a clearcut definition. Else it will end up like SOA.
The basis of the NoSQL systems lies in the random key - value pair. But this is not new. Traditional database systems like IMS and IDMS did support hashed ramdom keys (without making use of any index) and they still do. In fact IDMS already has a keyword NONSQL where they support SQL access to their older network database which they termed as NONSQL.
It's like Jacuzzi: both a brand and a generic name. It's not just a specific technology, but rather a specific type of technology, in this case referring to large-scale (often sparse) "databases" like Google's BigTable or CouchDB.
NoSQL the actual program appears to be a relational database implemented in awk using flat files on the backend. Though they profess, "NoSQL essentially has no arbitrary limits, and can work where other products can't. For example there is no limit on data field size, the number of columns, or file size" , I don't think it is the large scale database of the future.
As Joel says, massively scalable databases like BigTable or HBase, are much more interesting. GQL is the query language associated with BigTable and App Engine. It's largely SQL tweaked to avoid features Google considers bottle-necks (like joins). However, I haven't heard this referred to as "NoSQL" before.
NoSQL is a database system which doesn't use string based SQL queries to fetch data.
Instead you build queries using an API they will provide, for example Amazon DynamoDB is a good example of a NoSQL database.
NoSQL databases are better for large applications where scalability is important.
Does NoSQL mean non-relational database?
Yes, NoSQL is different from RDBMS and OLAP. It uses looser consistency models than traditional relational databases.
Consistency models are used in distributed systems like distributed shared memory systems or distributed data store.
How it works internally?
NoSQL database systems are often highly optimized for retrieval and appending operations and often offer little functionality beyond record storage (e.g. key-value stores). The reduced run-time flexibility compared to full SQL systems is compensated by marked gains in scalability and performance for certain data models.
It can work on Structured and Unstructured Data. It uses Collections instead of Tables
How do you query such "database"?
Watch SQL vs NoSQL: Battle of the Backends; it explains it all.
We recently are having major performance related issues in our current SQL Server DB.
Our application is pretty heavy on a single table we did some analysis and about 90% of our db data is in a single table. We run lot of queries on this table as well for analyticall purposes we are experiencing major performance issues now even with a single column addition sometimes slows our current Sp. Most of our teams are developers and we don't have access to a dba which might help in retuning our current db and make things work faster.
Cause of these constraints we are thinking of moving this part of the app to a NoSQL db.
My Questions are :
If this is the right direction we are heading ? As we are expecting exponential growth on this table. With loads of analytic's running on it.
Which would be best option for us CouchDB , Cassandra , MongoDB ? With stress on scalability and performance
For real time analysis and support similar to SQL how things work in a NoSQL is there a facility through which we can view current data being stored? I had read somewhere about Hadoop’s HIVE can be used to write and retreive data as SQL from NoSQL db's am I right?
What might be things which we would be losing out of while shifting from SQL to NoSQL ?
To your questions:
1.. If this is the right direction we are heading ? As we are expecting exponential growth on this table. With loads of analytic's running on it.
Yes, most of the noSQL systems are developed specifically to address scalability and availability, if you use them in the intended way.
2.. Which would be best option for us CouchDB , Cassandra , MongoDB ? With stress on scalability and performance
This depends entirely on what does your data looks like and how you will use it. The noSQL db you mentioned are implemented and behaves very differently from each other, see this link for a more detailed overview comparing the few you mentioned. Comparisons of noSQL solution
3.. For real time analysis and support similar to SQL how things work in a NoSQL is there a facility through which we can view current data being stored? I had read somewhere about Hadoop’s HIVE can be used to write and retreive data as SQL from NoSQL db's am I right?
This depends on the system you go with, because some noSQL db doesn't support range queries or joins, you are restricted in what you can view and how fast you can view.
4.. What might be things which we would be losing out of while shifting from SQL to NoSQL?
There are two major considerations for noSQL:
Query/Structure: NoSQL means no SQL. If your system actually requires structured and complex queries but you went with one of these cool new solution (especially a key-value storage, which is basically a giant hash table), you may soon find yourself in the middle of re-implementing a amateurish, ill-designed RDBMS, with all of your original problems.
Consistency: If you choose a eventual consistent system to scale horizontally, then you will have to accept your data being outdated, which may be harmless to some applications (forums?) or horrible in some other systems (bank).
I think you should stay relational and tune the table, its indexes, and the tables it joins to. You should also consider the use of aggregated (summarized data). Perhaps a more denormalized design would help or even re-designing the data into more of a star structure. Also, operational processing and decision support (or reporting) analyses should not be run on the same tables.
It might be possible to improve the SQL approach by checking for missing indexes etc and also seeing if the isolation level you are using is optimal. It may be possible to use snapshot isolation etc to improve performance. MSDN link
Read up on OLTP vs OLAP also.
NoSQL may still be a better option but you would still need to learn how to work with the database properly, it will come with another different set of issues.
I would like to test the NoSQL world. This is just curiosity, not an absolute need (yet).
I have read a few things about the differences between SQL and NoSQL databases. I'm convinced about the potential advantages, but I'm a little worried about cases where NoSQL is not applicable. If I understand NoSQL databases essentially miss ACID properties.
Can someone give an example of some real world operation (for example an e-commerce site, or a scientific application, or...) that an ACID relational database can handle but where a NoSQL database could fail miserably, either systematically with some kind of race condition or because of a power outage, etc ?
The perfect example will be something where there can't be any workaround without modifying the database engine. Examples where a NoSQL database just performs poorly will eventually be another question, but here I would like to see when theoretically we just can't use such technology.
Maybe finding such an example is database specific. If this is the case, let's take MongoDB to represent the NoSQL world.
Edit:
to clarify this question I don't want a debate about which kind of database is better for certain cases. I want to know if this technology can be an absolute dead-end in some cases because no matter how hard we try some kind of features that a SQL database provide cannot be implemented on top of nosql stores.
Since there are many nosql stores available I can accept to pick an existing nosql store as a support but what interest me most is the minimum subset of features a store should provide to be able to implement higher level features (like can transactions be implemented with a store that don't provide X...).
This question is a bit like asking what kind of program cannot be written in an imperative/functional language. Any Turing-complete language and express every program that can be solved by a Turing Maching. The question is do you as a programmer really want to write a accounting system for a fortune 500 company in non-portable machine instructions.
In the end, NoSQL can do anything SQL based engines can, the difference is you as a programmer may be responsible for logic in something Like Redis that MySQL gives you for free. SQL databases take a very conservative view of data integrity. The NoSQL movement relaxes those standards to gain better scalability, and to make tasks that are common to Web Applications easier.
MongoDB (my current preference) makes replication and sharding (horizontal scaling) easy, inserts very fast and drops the requirement for a strict scheme. In exchange users of MongoDB must code around slower queries when an index is not present, implement transactional logic in the app (perhaps with three phase commits), and we take a hit on storage efficiency.
CouchDB has similar trade-offs but also sacrifices ad-hoc queries for the ability to work with data off-line then sync with a server.
Redis and other key value stores require the programmer to write much of the index and join logic that is built in to SQL databases. In exchange an application can leverage domain knowledge about its data to make indexes and joins more efficient then the general solution the SQL would require. Redis also require all data to fit in RAM but in exchange gives performance on par with Memcache.
In the end you really can do everything MySQL or Postgres do with nothing more then the OS file system commands (after all that is how the people that wrote these database engines did it). It all comes down to what you want the data store to do for you and what you are willing to give up in return.
Good question. First a clarification. While the field of relational stores is held together by a rather solid foundation of principles, with each vendor choosing to add value in features or pricing, the non-relational (nosql) field is far more heterogeneous.
There are document stores (MongoDB, CouchDB) which are great for content management and similar situations where you have a flat set of variable attributes that you want to build around a topic. Take site-customization. Using a document store to manage custom attributes that define the way a user wants to see his/her page is well suited to the platform. Despite their marketing hype, these stores don't tend to scale into terabytes that well. It can be done, but it's not ideal. MongoDB has a lot of features found in relational databases, such as dynamic indexes (up to 40 per collection/table). CouchDB is built to be absolutely recoverable in the event of failure.
There are key/value stores (Cassandra, HBase...) that are great for highly-distributed storage. Cassandra for low-latency, HBase for higher-latency. The trick with these is that you have to define your query needs before you start putting data in. They're not efficient for dynamic queries against any attribute. For instance, if you are building a customer event logging service, you'd want to set your key on the customer's unique attribute. From there, you could push various log structures into your store and retrieve all logs by customer key on demand. It would be far more expensive, however, to try to go through the logs looking for log events where the type was "failure" unless you decided to make that your secondary key. One other thing: The last time I looked at Cassandra, you couldn't run regexp inside the M/R query. Means that, if you wanted to look for patterns in a field, you'd have to pull all instances of that field and then run it through a regexp to find the tuples you wanted.
Graph databases are very different from the two above. Relations between items(objects, tuples, elements) are fluid. They don't scale into terabytes, but that's not what they are designed for. They are great for asking questions like "hey, how many of my users lik the color green? Of those, how many live in California?" With a relational database, you would have a static structure. With a graph database (I'm oversimplifying, of course), you have attributes and objects. You connect them as makes sense, without schema enforcement.
I wouldn't put anything critical into a non-relational store. Commerce, for instance, where you want guarantees that a transaction is complete before delivering the product. You want guaranteed integrity (or at least the best chance of guaranteed integrity). If a user loses his/her site-customization settings, no big deal. If you lose a commerce transation, big deal. There may be some who disagree.
I also wouldn't put complex structures into any of the above non-relational stores. They don't do joins well at-scale. And, that's okay because it's not the way they're supposed to work. Where you might put an identity for address_type into a customer_address table in a relational system, you would want to embed the address_type information in a customer tuple stored in a document or key/value. Data efficiency is not the domain of the document or key/value store. The point is distribution and pure speed. The sacrifice is footprint.
There are other subtypes of the family of stores labeled as "nosql" that I haven't covered here. There are a ton (122 at last count) different projects focused on non-relational solutions to data problems of various types. Riak is yet another one that I keep hearing about and can't wait to try out.
And here's the trick. The big-dollar relational vendors have been watching and chances are, they're all building or planning to build their own non-relational solutions to tie in with their products. Over the next couple years, if not sooner, we'll see the movement mature, large companies buy up the best of breed and relational vendors start offering integrated solutions, for those that haven't already.
It's an extremely exciting time to work in the field of data management. You should try a few of these out. You can download Couch or Mongo and have them up and running in minutes. HBase is a bit harder.
In any case, I hope I've informed without confusing, that I have enlightened without significant bias or error.
RDBMSes are good at joins, NoSQL engines usually aren't.
NoSQL engines is good at distributed scalability, RDBMSes usually aren't.
RDBMSes are good at data validation coinstraints, NoSQL engines usually aren't.
NoSQL engines are good at flexible and schema-less approaches, RDBMSes usually aren't.
Both approaches can solve either set of problems; the difference is in efficiency.
Probably answer to your question is that mongodb can handle any task (and sql too). But in some cases better to choose mongodb, in others sql database. About advantages and disadvantages you can read here.
Also as #Dmitry said mongodb open door for easy horizontal and vertical scaling with replication & sharding.
RDBMS enforce strong consistency while most no-sql are eventual consistent. So at a given point in time when data is read from a no-sql DB it might not represent the most up-to-date copy of that data.
A common example is a bank transaction, when a user withdraw money, node A is updated with this event, if at the same time node B is queried for this user's balance, it can return an outdated balance. This can't happen in RDBMS as the consistency attribute guarantees that data is updated before it can be read.
RDBMs are really good for quickly aggregating sums, averages, etc. from tables. e.g. SELECT SUM(x) FROM y WHERE z. It's something that is surprisingly hard to do in most NoSQL databases, if you want an answer at once. Some NoSQL stores provide map/reduce as a way of solving the same thing, but it is not real time in the same way it is in the SQL world.
There was an article on Hacker News a couple of days ago that reached first page titled something like
"2 cases when not to use Mongodb" but I really can't find it anymore...
Does anyone know where I can find the above described article?
What cases are there when NoSQL fails?
We use MongoDB for storing tons and tons of analytics data for which we don't care if some stuff occasionally gets lost in a server crash. The data really fits MongoDB well and it would have been a nightmare if we were to use an SQL database for this. But for bank transactions we wouldn't even consider MongoDB.
The write lock might be a problem for some people. On the other hand MongoDB supports easy sharding, much easier than with SQL. Sharding allows us to scale horizontally which is a huge plus for our data.
http://news.ycombinator.com/item?id=1691748
By any reasonable definition "NoSQL" ought to include non-SQL RDBMSs in its scope (because there's no sound reason why the relational model can't address the same requirements as other NoSQL models). If you accept that, then there is no limit to what NoSQL DBMSs could do. We would have no more need of SQL - ever!
Sadly, there seems to be a common assumption among NoSQL thought leaders that "NoSQL" has to mean "not relational". That is highly unfortunate because if the relational model is ignored then NoSQL is never likely to replace SQL for many purposes. (I take it for granted that finding a long-term, relational model replacement for SQL would actually be a good thing :)
You don't want to use NoSQL typically when you....
... don't want to use SQL! /hardy har har
Most of the NoSQL solutions I've seen seem to fall in the key-value store approach, and aren't relational. They tend to give up ACID properties.
So when you evaluate a database system, when you don't need ACID, when you don't want relational algebra, when you do have a need for a KV store, then the NoSQL approach is your friend.
Note too that there is a wide variety of 'NoSQL' systems, and they all are busily working on slightly different approaches.
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.