HBase and other column-oriented databases are often compared to more traditional and popular relational databases or RDBMSs. Although they differ dramatically in their implementations and in what they set out to accomplish, the fact that they are potential solutions to the same problems means that despite their enormous differences, the comparison is a fair one to make.
As described previously, HBase is a distributed, column-oriented data storage system. It picks up where Hadoop left off by providing random reads and writes on top of HDFS. It has been designed from the ground up with a focus on scale in every direction: tall in numbers of rows (billions), wide in numbers of columns (millions), and to be horizontally partitioned and replicated across thousands of commodity nodes automatically.
The table schemas mirror the physical storage, creating a system for efficient data structure serialization, storage, and retrieval. The burden is on the application developer to make use of this storage and retrieval in the right way.
Strictly speaking, an RDBMS is a database that follows Codd’s 12 Rules. Typical RDBMSs are fixed-schema, row-oriented databases with ACID properties and a sophisticated SQL query engine. The emphasis is on strong consistency, referential integrity, abstraction from the physical layer, and complex queries through the SQL language.
You can easily create secondary indexes, perform complex inner and outer joins, count, sum, sort, group, and page your data across a number of tables, rows, and columns.
For a majority of small- to medium-volume applications, there is no substitute for the ease of use, flexibility, maturity, and powerful feature set of available open source RDBMS solutions like MySQL and PostgreSQL. However, if you need to scale up in terms of dataset size, read/write concurrency, or both, you’ll soon find that the conveniences of an RDBMS come at an enormous performance penalty and make distributioninherently difficult. The scaling of an RDBMS usually involves breaking Codd’s rules, loosening ACID restrictions, forgetting conventional DBA wisdom, and on the way losing most of the desirable properties that made relational databases so convenient in the first place.
Here is a synopsis of how the typical RDBMS scaling story runs. The following list presumes a successful growing service:
Initial public launch
Move from local workstation to shared, remote hosted MySQL instance with a well-defined schema.
Service becomes more popular; too many reads hitting the database
Add memcached to cache common queries. Reads are now no longer strictly ACID; cached data must expire.
Service continues to grow in popularity; too many writes hitting the database
Scale MySQL vertically by buying a beefed up server with 16 cores, 128 GB of RAM, and banks of 15 k RPM hard drives. Costly.
New features increases query complexity; now we have too many joins
Denormalize your data to reduce joins. (That’s not what they taught me in DBA school!)
Rising popularity swamps the server; things are too slow
Stop doing any server-side computations.
Some queries are still too slow
Periodically prematerialize the most complex queries, try to stop joining in most cases.
Reads are OK, but writes are getting slower and slower
Drop secondary indexes and triggers (no indexes?).
At this point, there are no clear solutions for how to solve your scaling problems. In any case, you’ll need to begin to scale horizontally. You can attempt to build some type of partitioning on your largest tables, or look into some of the commercial solutions that provide multiple master capabilities.Countless applications, businesses, and websites have successfully achieved scalable, fault-tolerant, and distributed data systems built on top of RDBMSs and are likely using many of the previous strategies. But what you end up with is something that is no longer a true RDBMS, sacrificing features and conveniences for compromises and complexities.
Any form of slave replication or external caching introduces weak consistency into your now denormalized data. The inefficiency of joins and secondary indexes means almost all queries become primary key lookups. A multiwriter setup likely means no real joins at all and distributed transactions are a nightmare. There’s now an incredibly complex network topology to manage with an entirely separate cluster for caching. Even with this system and the compromises made, you will still worry about your primary master crashing and the daunting possibility of having 10 times the data and 10 times the load in a few months.
Enter HBase, which has the following characteristics:
No real indexes
Rows are stored sequentially, as are the columns within each row. Therefore, no issues with index bloat, and insert performance is independent of table size.
As your tables grow, they will automatically be split into regions and distributed across all available nodes.
Scale linearly and automatically with new nodes
Add a node, point it to the existing cluster, and run the regionserver. Regions will automatically rebalance and load will spread evenly.
Clusters are built on $1,000–$5,000 nodes rather than $50,000 nodes. RDBMSs are I/O hungry, requiring more costly hardware.
Lots of nodes means each is relatively insignificant. No need to worry about individual node downtime.
MapReduce integration allows fully parallel, distributed jobs against your data with locality awareness.
If you stay up at night worrying about your database (uptime, scale, or speed), then you should seriously consider making a jump from the RDBMS world to HBase. Utilize a solution that was intended to scale rather than a solution based on stripping down and throwing money at what used to work. With HBase, the software is free, the hardware is cheap, and the distribution is intrinsic.
Use Case: HBase at Streamy.com
Streamy.com is a real-time news aggregator and social sharing platform. With a broad feature set, we started out with a complex implementation on top of PostgreSQL. It’s a terrific product with a great community and a beautiful codebase. We tried every trick in the book to keep things fast as we scaled, going so far as to modify the code directly to suit our needs. Originally taking advantage of all RDBMS goodies, we found that eventually, one by one, we had to let them all go. Along the way, our entire team became the DBA.
We did manage to solve many of the issues that we ran into, but there were two that eventually led to the decision to find another solution from outside the world of RDBMS.
Streamy crawls thousands of RSS feeds and aggregates hundreds of millions of items from them. In addition to having to store these items, one of our more complex queries reads a time-ordered list of all items from a set of sources. At the high end, this can run to several thousand sources and all of their items all in a single query.
Very large items tables
At first, this was a single items table, but the high number of secondary indexes made inserts and updates very slow. We started to divide items up into several one-to-one link tables to store other information, separating static fields from dynamic ones, grouping fields based on how they were queried, and denormalizing everything along the way. Even with these changes, single updates required rewriting the entire record,so tracking statistics on items was difficult to scale. The rewriting of records and having to update indexes along the way are intrinsic properties of the RDBMS we were using.
They could not be decoupled. We partitioned our tables, which was not too difficult because of the natural partition of time, but the complexity got out of hand fast. We needed another solution!
Very large sort merges
Performing sorted merges of time-ordered lists is common in many Web 2.0 applications. An example SQL query might look like this:
Assuming id is a primary key on streams, and that stamp and type have secondary indexes, an RDBMS query planner treats this query as follows:
The problem here is that we are after only the top 10 IDs, but the query planner actually materializes an entire merge and then limits at the end. A simple heapsort across each of the types would allow you to “early out” once you have the top 10. In our case, each type could have tens of thousands of IDs in it, so materializing the entire list and sorting it was extremely slow and unnecessary. We actually went so far as to write a custom PL/Python script that performed a heapsort using a series of queries like the following:
If we ended up taking from typeN (it was the next most recent in the heap), we would run another query:
In nearly all cases, this outperformed the native SQL implementation and the query planner’s strategy. In the worst cases for SQL, we were more than an order of magnitude faster using the Python procedure. We found ourselves continually trying to outsmart the query planner.
Again, at this point, we really needed another solution.
Life with HBase
Our RDBMS-based system was always capable of correctly implementing our requirements; the issue was scaling. When you start to focus on scale and performance rather than correctness, you end up short-cutting and optimizing for your domain-specific use cases everywhere possible. Once you start implementing your own solutions to your data problems, the overhead and complexity of an RDBMS gets in your way. Theabstraction from the storage layer and ACID requirements are an enormous barrier and luxury that you cannot always afford when building for scale. HBase is a distributed, column-oriented, sorted map store and not much else. The only major part that is abstracted from the user is the distribution, and that’s exactly what we don’t want to deal with. Business logic, on the other hand, is very specialized and optimized. WithHBase not trying to solve all of our problems, we’ve been able to solve them better ourselves and rely on HBase for scaling our storage, not our logic. It was an extremely liberating experience to be able to focus on our applications and logic rather than the scaling of the data itself.
We currently have tables with hundreds of millions of rows and tens of thousands of columns; the thought of storing billions of rows and millions of columns is exciting, not scary.
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