Overview of the Oracle NoSQL Database

Oracle is the clear market leader in the commercial database community, and therefore it is critical for any member of the database community to pay close attention to the new product announcements coming out of Oracle’s annual Open World conference. The sheer size of Oracle’s sales force, entrenched customer base, and third-party ecosystem instantly gives any new Oracle product the potential for very high impact. Oracle’s new products require significant attention simply because they’re made by Oracle.

I was particularly eager for this year’s Oracle Open World conference, because there were rumors of two separate new Oracle products involving Hadoop and NoSQL --- two of the central research focuses of my database group at Yale --- one of them (Hadoop) also being the focus of my recent startup (Hadapt). Oracle’s Hadoop announcements, while very interesting from a business perspective (everyone is talking about how this “validates” Hadoop), are not so interesting from a technical perspective (the announcements seem to revolve around (1) creating a “connector” between Hadoop and Oracle, where Hadoop is used for ETL tasks, and the output of these tasks are then loaded over this connector to the Oracle DBMS and (2) packaging the whole thing into an appliance, which again is very important from a business perspective since there is certainly a market for anything that makes Hadoop easier to use, but does not seem to be introducing any technically interesting new contributions).

In contrast, the Oracle NoSQL database is actually a brand new system built by the Oracle BerkeleyDB team, and is therefore very interesting from a technical perspective. I therefore spent way too much time trying to find out as much as I could about this new system from a variety of sources. There is not yet a lot of publicly available information about the system; however there is a useful whitepaper written by the illustrious Harvard professor Margo Seltzer, who has been working with Oracle since they acquired her start-up in 2006 (the aforementioned BerkeleyDB).

Due to the dearth of available information on the system, I thought that it would be helpful to the readers of my blog if I provided an overview of what I’ve learned about it so far. Some of the facts I state below have been directly made by Oracle; other facts are inferences that I’ve made, based on my understanding of the system architecture and implementation. As always, if I have made any mistakes in my inferences, please let me know, and I will fix them as soon as possible.

The coolest thing about the Oracle NoSQL database is that it is not a simple copy of a currently existing NoSQL system. It is not Dynamo or SimpleDB. It is not Bigtable or HBase. It is not Cassandra or Riak. It is not MongoDB or CouchDB. It is a new system that has a chosen a different point (actually --- several different points) in the system-design tradeoff space than any of the above mentioned systems. Since it makes a different set of tradeoffs, it is entirely inappropriate to call it “better” or “worse” than any of these systems. There will be situations where the Oracle solution will be more appropriate, and there will be situations where other systems will be more appropriate.

Overview of the system:
Oracle NoSQL database is a distributed, replicated key-value store. Given a cluster of machines (in a shared-nothing architecture, with each machine having its own storage, CPU, and memory), each key-value pair is placed on several of these machines depending on the result of a hash function on the key. In particular, the key-value pair will be placed on a single master node, and a configurable number of replica nodes. All write and update operations for a key-value pair go to the master node for that pair first, and then all replica nodes afterwards. This replication is typically done asynchronously, but it is possible to request that it be done synchronously if one is willing to tolerate the higher latency costs. Read operations can go to any node if the user doesn’t mind incomplete consistency guarantees (i.e. reads might not see the most recent data), but they must be served from the master node if the user requires the most recent value for a data item (unless replication is done synchronously). There is no SQL interface (it is a NoSQL system after all!) --- rather it supports simple insert, update, and delete operations of key-value pairs.

The following is where the Oracle NoSQL Database falls in various key dimensions:

CAP
Like many NoSQL databases, the Oracle NoSQL Database is configurable to be either C/P or A/P in CAP. In particular, if writes are configured to be performed synchronously to all replicas, it is C/P in CAP --- a partition or node failure causes the system to be unavailable for writes. If replication is performed asynchronously, and reads are configured to be served from any replica, it is A/P in CAP --- the system is always available, but there is no guarantee of consistency. [Edit: Actually this configuration is really just P of CAP --- minority partitions become unavailable for writes (see comments about eventual consistency below). This violates the technical definition of "availability" in CAP. However, it is obviously the case that the system still has more availability in this case than the synchronous write configuration.]

Eventual consistency
Unlike Dynamo, SimpleDB, Cassandra, or Riak, the Oracle NoSQL Database does not support eventual consistency. I found this to be extremely amusing, since Oracle’s marketing material associates NoSQL with the BASE acronym. But the E in BASE stands for eventual consistency! So by Oracle’s own definition, their lack of support of eventual consistency means that their NoSQL Database is not actually a NoSQL Database! (In my opinion, their database is really NoSQL --- they just need to fix their marketing literature that associates NoSQL with BASE). My proof for why the Oracle NoSQL Database does not support eventual consistency is the following: Let’s say the master node for a particular key-value pair fails, or a network partition separates the master node from its replica nodes. The key-value pair becomes unavailable for writes for a short time until the system elects a new master node from the replicas. Writes can then continue at the new master node. However, any writes that had been submitted to the old master node, but had not yet been sent to the replicas before the master node failure (or partition) are lost. In an eventually consistent system, these old writes can be reconciled with the current state of the key-value pair after the failed node recovers its log from stable storage, or when the network partition is repaired. Of course, if replication had been configured to be done synchronously (at a cost of latency), there will not be data loss during network partitions or node failures. Therefore, there is a fundamental difference between the Oracle NoSQL database system and eventually consistent NoSQL systems: while eventually consistent NoSQL systems choose to tradeoff consistency for latency and availability during failure and network partition events, the Oracle NoSQL system instead trades of durability for latency and availability. To be clear, this difference is only for inserts and updates --- the Oracle NoSQL database is able to trade-off consistency for latency on read requests --- it supports similar types of timeline consistency tradeoffs as the Yahoo PNUTs/Sherpa system.

[Two of the members of the Oracle NoSQL Database team have commented below. There is a little bit of a debate about my statement that the Oracle NoSQL Database lacks eventual consistency, but I stand by the text I wrote above. For more, see the comments.]

Joins
Like most NoSQL systems, the Oracle NoSQL database does not support joins. It only supports simple read, write, update, and delete operations on key-value pairs.

Data Model
The Oracle NoSQL database actually has a more subtle data model than simple key-value pairs. In particular, the key is broken down into a “major key path” and “minor key path” where all keys with the same “major key path” are guaranteed to be stored on the same physical node. I expect that the way minor keys will be used in the Oracle NoSQL database will map directly to the way column families are used in Bigtable, HBase and Cassandra. Rather than trying to gather together every possible attribute about a key in a giant “value” for the single key-value pair, you can separate them into separate key-value pairs where the “major key path” is the same for all the keys in the set of key-value pairs, but the “minor key path” will be different. This is similar to how column families for the same key in Bigtable, HBase, and Cassandra can also be stored separately. Personally, I find the major and minor key path model to be more elegant than the column family model (I have ranted against column-families in the past).

ACID compliance
Like most NoSQL systems, the Oracle NoSQL database is not ACID compliant. Besides the durability and consistency tradeoffs mentioned above, the Oracle NoSQL database also does not support arbitrary atomic transactions (the A in ACID). However, it does support atomic operations on the same key, and even allows atomic transactions on sets of keys that share the same major key path (since keys that share the same major key path are guaranteed to be stored on the same node, atomic operations can be performed without having to worry about distributed commit protocols across multiple machines).

Summary
The sweet spot for the Oracle NoSQL database seems to be in single-rack deployments (e.g. the Oracle Big Data appliance) with a low-latency network, so that the system can be set up to use synchronous replication while keeping latency costs of this type of replication small (and the probability of network partitions are small). Another sweet spot is for wider area deployments, but the application is able to work around reduced durability guarantees. It therefore seems to present the largest amount of competition for NoSQL databases like MongoDB which have similar sweet spots. However, the Oracle NoSQL database will need to add additional “developer-friendly” features if it wants to compete head-to-head with MongoDB. Either way, there are clearly situations where the Oracle NoSQL database will be a great fit, and I love that Oracle (in particular, the Oracle BerkeleyDB team) built this system from scratch as an interesting and technically distinct alternative to currently available NoSQL systems. I hope Oracle continues to invest in the system and the team behind it.