Courtesy: Dave Page
I’ve been asked a few times recently for my opinion on VoltDB, the new database server architected by the ‘father’ of Postgres, Dr Michael Stonebraker so rather than repeating myself over and over again it seems like a good idea to write it all down.
VoltDB is an in-memory, lockless relational database that maintains ACID compliance, has a SQL interface and claims to offer massive performance increases and scalability over ‘traditional’ relational databases. If you take the time to visit the website, and download some of the docs or even the product itself, if you’re a database geek like me you’ll probably be pretty impressed. The technology is interesting – the ability to avoid locking seems like paradise, as does the linear scaling.
There are downsides though. In order to implement the key features of the DBMS, Stonebraker has had to design the system to work with a pretty narrow set of use-cases. Lets consider some of the pros and cons and compare them to Postgres.
In memory database
VoltDB is an in-memory database. This means it can be very fast. It also gives us two potential problems:
The database must fit into the available memory on the system. That means that with a single server with 4GB of RAM, a practical database size limit may be in the order of 3GB or so, once you allow 1GB for the OS and memory required to actually operate the database server.
Durability (the D in ACID) must be provided through replication of data to one or more secondary servers over the network, or through writing periodic snapshots to disk. Because replication is synchronous within the cluster and asynchronous between clusters, it is possible for a cluster-wide power failure to cause the loss of committed transactions.
Of course, in some circumstances these may be non-issues. VoltDB scales horizontally extremely well (near linearly in fact), so if your database is large, you can add more servers to get the storage you need. This won’t suit people running multi-terabyte databases of course – RAM is cheap these days, but not that cheap – especially when you multiply by 2 or more for durability and redundancy!
In contrast, Postgres stores it’s data on non-volatile storage – a direct attached hard disk, or SAN for example. The issues here are:
Since Postgres uses both a shared buffer cache and the kernel cache (and potentially Infinite Cache in EnterpriseDB’s Postgres Plus Advanced Server), in a well-configured system – like VoltDB – it will read most if not all of its data from memory. Unlike VoltDB, Postgres will still have locking and buffer management overhead however and of course, any disk reads will be much slower.
Durability is achieved through the immediate logging of all transactions to a sequential transaction log, and later writing of updated pages to the heap. This is much slower than VoltDB’s in-memory operations as we write everything to disk twice.
Summary: VoltDB is fast, because it’s in-memory. This creates serious practical limits on database size though – how big is your budget? You’ll also need at least two servers, with independent power supplies for any real durability. Postgres is slower, most noticeably when the working set doesn’t fit into a cache, but you can store multi-terabyte databases on very cheap hard disks with full durability.
Traditional DBMSs allow for concurrent access by using granular locking and other techniques such as MVCC (Multi Version Concurrency Control). By locking only the smallest part of the database required to perform a specific operation, they ensure that other users can still access the rest of the database concurrently. Locks are held for as short a period as possible.
In contrast, VoltDB is a lockless database and therefore doesn’t suffer from any of the complexities involved in lock management or MVCC snapshot management that Postgres does. This is especially important when scaling horizontally, as lock management and snapshot management are two of the most complex problems to solve when building clustered database systems based on Postgres.
VoltDB’s solution isn’t a panacea for these issues though. To achieve lockless operation, every request is serialised through each partition of the database. This means that if you have one partition (i.e. a single server), each database request will be run sequentially, with no parallelism at all. If you have partitioned data, then requests that affect different individual partitions may run in parallel, however any multi-partition requests must be run against the entire cluster on their own.
So what does this mean? Primarily, the effect is that performance is likely to tank if you try to run any complex queries on the system. Any long running, multi-partition query will block all other users of the database until it completes, which means that the system is only suitable for simple OLTP applications, with well thought out data structures. Don’t be tempted to sneak any reports into your apps!
In contrast, Postgres can easily handle both OLTP and DW (data warehouse) workloads. MVCC ensures that ‘readers never block writers’, so you can run complex reports at the same time as tens or hundreds of OLTP operations are running concurrently. The downside is, that it is significantly harder to build multi-master clusters with Postgres.
Summary: VoltDB’s lockless architecture makes it easy to scale horizontally, but limits concurrency. Postgres is harder to scale out, but offers excellent concurrency through fine-grained locking and MVCC.
Evolution and growth
The schema in a VoltDB database is defined in a runtime ‘catalog’. This includes not just the schema, but also the details of the different nodes in the cluster, and the size of the database. Any changes that are required to the schema, to the configuration of the cluster, or to the size of the database currently require:
Dump of the data to non-volatile storage and shutdown of the VoltDB server on each node.
Reconfiguration of the runtime catalog.
Restart of the VoltDB server on each node, and reload of the data from storage.
In Postgres, database objects can be modified entirely ‘on the fly’, without shutting down the server or dumping or reloading data. The database size can grow to theoretically unlimited sizes, constrained only by the amount of storage available without the need to restart. Most of the clustering solutions for Postgres don’t require a shutdown to install or reconfigure them.
Summary: VoltDB cannot offer high availability if the database grows beyond the predicted size, nor can changes to the schema (such as those that may be required by a software upgrade) be made without shutting down the system. Postgres allows your system to grow and evolve without requiring downtime.
Ecosystem, drivers and tools
VoltDB is a new project. At present, it doesn’t have any real community, the only tools available are those shipping with the server, and the API is based on calling Java stored procedures. Actually, that’s a bit awkward really. You essentially have to write a data access layer on the server in Java, which encapsulates all of the SQL queries you need to run. Your client code then calls those procedures directly.
Postgres has a long history. There is a large and vibrant community, with hundreds of tools, add-ons, utilities and so-on available. APIs to access the database are available for a host of languages including C, C++, .NET, Perl, Python, TCL, Ruby, PHP, Java and more. Stored procedures (functions) can be written in common languages including C, Perl, TCL, Ruby, Python PHP and Java.
Summary: The long Postgres history means that there are far more tools, utilities and interfaces available for it. VoltDB can catch up, but it will likely take many years. Further, the Java API limits how you can access the database – there is no way to connect via a standard ODBC or JDBC driver to allow you to use generic query tools for example.
VoltDB is an interesting product, but one with limited use-cases. If your database can fit in the memory of your server cluster, and you can architect your application to avoid any kind of complex query then it can offer vast performance advantages over traditional DBMSs like Postgres, and has potential to scale extremely well.
For most users though, concurrency and the ability to run complex queries are real issues, as is the ability to scale the database beyond sizes that are economical to keep in RAM, without having to dump and reload the data and restart the server to accommodate expansion and evolution of the system.
I can see some interesting use cases for VoltDB, and I’ll be keeping an eye on its evolution and time permitting, trying it out for size. It’s by no means a universal replacement for Postgres or any other similar DBMS (nor does it claim to be), but it could prove to be a very useful tool in the right situation.