Big Database (Part 3): The Future of OLTP
Big Database (Part 3): The Future of OLTP
If you have a web-scale, OLTP and/or real-time analytics requirement, the NewSQL class of databases need serious consideration.
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The World Has Changed
The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now, billions of people are using to their smartphone or tablet 24/7 to buy just about everything, and they’re interacting with Facebook, Twitter, and Instagram. The pace has been unstoppable.
Expectations have also changed. If a web page doesn’t refresh within seconds, we’re quickly frustrated and go elsewhere. If a website is down, we fear it’s the end of civilization as we know it. If a major site is down, it makes global headlines.
Instant gratification takes too long! — Ladawn Clare-Panton
The above leads to a few observations:
- Scalability: With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions
- High availability: IT systems must run 24/7 and be resilient to failure. (A failure at Bank of America in 2011 affected 29 million customers over six days.)
- High performance: In tandem with incremental scalability, performance must remain stable and fast. At the extreme end, Amazon estimates it loses $1.6B a year for each additional second it takes a page to load.
- Velocity: As web-connected sensors are increasingly built into machines (your smartphone being the obvious one), transactions can repeatedly arrive at millions of transactions per second.
- Real-time analytics: Nightly batch processing and business intelligence is no longer acceptable. The line between analytic and operational processing is becoming blurred, and increasingly, there are demands for real-time decision-making.
The Internet of Things is sending velocity through the roof! — Dr. Stonebraker (MIT)
The above demands have lead to the truly awful marketing term translytical databases, which refer to hybrid solutions that handle both high throughput transactions and real-time analytics in the same solution.
What’s the Problem?
The challenge faced by all database vendors is to provide high-performance solutions while reducing costs (perhaps using commodity servers). But there are conflicting demands:
- Performance: To minimize latency and process transactions in milliseconds.
- Availability: The ability to keep going, even if one or more nodes in the system fail or are temporarily disconnected from the network.
- Scalability: The ability to incrementally scale to massive data volumes and transaction velocity.
- Consistency: To provide consistent, accurate results — particularly in the event of network failures.
- Durability: To ensure changes once committed are not lost.
- Flexibility: Providing a general-purpose database solution to support both transactional and analytic workloads.
The only realistic way to provide massive incremental scalability is to deploy a scale-out distributed system. Typically, to maximize availability, changes applied on one node are immediately replicated to two or more others. However, once you distribute data across servers, you face trade-offs.
Performance vs. Availability and Durability
Many NoSQL databases replicate data to other nodes in the cluster to improve availability. If immediately following a write, the database node crashes, the data is available on other machines and changes are, therefore, durable. It’s possible, however, to relax this requirement and return immediately. This maximizes performance at the risk of losing the change. The change may not be durable, after all.
Consistency vs. Availability
NoSQL databases support eventual consistency. For example, in the above diagram, if network connectivity to New York temporarily fails, there are two options:
- Stop processing: But availability suffers in New York.
- Accept reads/writes: And resolve the differences once reconnected — but this risks giving out-of-date or incorrect results, and conflicting writes need to be resolved.
Clearly, we trade consistency for availability.
Flexibility vs. Scalability
Compared to general-purpose relational systems like Oracle and DB2, NoSQL databases are relatively inflexible and don’t (for example) support join operations. In addition to many not supporting the SQL language, some (i.e. Neo4J and MongoDB) are designed to support specific problem spaces: graph processing and JSON data structures.
Even databases like HBase, Cassandra, and Redis abandon relational joins and many limit access to a single primary key with no support for secondary indexes.
Many databases claim 100% ACID transactions. In reality few provide formal ACID guarantees. — Dr. Peter Bailis (University of Stanford)
ACID vs. Eventual Consistency
One of the major challenges in scaling database solutions is maintaining ACID consistency. Amazon solved the performance problem with the DynamoDB database by relaxing the consistency constraints in favor of speed, which led to a raft of NoSQL databases.
As an aside, even the most successful databases (including Oracle) don’t provide true ACID isolation. Of 18 databases surveyed, only three databases (VoltDB, Ingres, and Berkeley DB) were found to support serializability by default. The primary reason is it’s difficult to achieve while maintaining performance.
Eventual consistency is a particularly weak model. The system can return any data, and still be eventually consistent. — Dr. Peter Bailis (Stanford)
Eventual consistency, on the other hand, provides almost no consistency guarantees. The diagram below illustrates a problem whereby one user deducts $1m from a bank account but before the changes are replicated, a second user checks the balance. The only guarantee is that (provided there are no further writes) the system will eventually provide a consistent result. How this even useful, let alone acceptable?
The OLTP Database Reimagined
Ten years ago, Dr. Michael Stonebraker wrote the paper The End of an Architectural Era, where he argued the 1970s architecture of databases from Oracle, Microsoft, and IBM was no longer fit for purpose.
He stated that an OLTP database should be:
- Dedicated to a single problem: To quickly execute short-lived, predefined (not ad-hoc) transactions with a relatively simple query plan. In short, a dedicated OLTP platform.
- ACID-compliant: With all transactions running single threaded, providing full serializability by default.
- Always available: Using data replication (not a hot standby) to provide high availability at almost no additional cost.
- Geographically distributed: Running seamlessly on a grid of dispersed machines (adding further to resilience and providing local performance benefits).
- A shared-nothing architecture: With load dispersed across multiple machines connected as a peer-to-peer grid. Adding a machine is a seamless operation with zero downtime and loss of a node results in a marginal performance degradation rather than full system downtime.
- Memory-based: Entirely run in memory for absolute speed, with durability provided by in-memory data replication other nodes.
- Eliminate bottlenecks: Achieve massive throughput by completely redesigning the database internals to run single-threaded while removing the need for redo logs, locking, and latching: the most significant constraints on database performance.
To demonstrate the above was feasible, he built a prototype, the H-Store database, and demonstrated a TPC-C benchmark performance of 82x faster than an unnamed commercial rival on the same hardware. The H-Store prototype achieved a remarkable 70,000 transactions per second compared to just 850 from the commercial rival, despite significant DBA tuning effort.
Achieving the Impossible!
Dr. Stonebraker’s achievement is remarkable. The previous TCP-C world record was around 1,000 transactions per CPU core, and yet H-Store achieved 35x that on a dual-core 2.8GHz desktop machine. In his 2008 paper OLTP Through the Looking Glass, he went on explain why commercial databases (including Oracle) perform so badly.
The diagram above illustrates the 93% overhead built into a traditional (legacy?) database including locking, latching, and buffer management. In total, just 7% of machine resources are dedicated to the task at hand.
H-Store was able to achieve the seemingly impossible task of full ACID transactional consistency orders of magnitude faster by simply eliminating these bottlenecks and using memory rather than disk-based processing.
NewSQL Database Technology
First released in 2010, VoltDB is the commercial implementation of the H-Store prototype and is a dedicated OLTP platform for web-scale transaction processing and real-time analytics. As this infographic demonstrates, there are 250 commercially available database solutions, of which just 13 are classified as NewSQL technology.
In common with other NewSQL databases, VoltDB aims to run entirely in-memory with optional periodic disk snapshots. It runs on 64 bit Linux on premises, AWS, Google, and Azure cloud services and implements a horizontally scalable architecture.
Unlike traditional relational databases where data is written to disk-based log files, VoltDB applies changes in parallel to multiple machines in memory. For example, a K-Safety of two guarantees no data loss even if two machines fail, as data is committed to at least three in-memory nodes.
Transactions are submitted as Java stored procedures that can be executed asynchronously in the database, and data is automatically partitioned (sharded) across nodes in the system, although reference data can be duplicated to maximize join performance. Unusually, VoltDB also supports semi-structured data in the form of JSON data structures.
In terms of performance, a 2015 benchmark demonstrates VoltDB at almost double the processing speed of NoSQL database Cassandra, while also six times less expensive in AWS cloud processing costs.
Finally, VoltDB version 6.4 passed the remarkably stringent Jepsen distributed safety tests.
To put this in context, a previous test with NoSQL database Riak demonstrated dropping 30-70% of writes even with the strongest consistency setting. Meanwhile, Cassandra lost up to 5% of writes using lightweight transactions.
In common with VoltDB, MemSQL is a scale-out, in-memory distributed database designed for fast data ingestion and real-time analytics. It also runs on-premises and on the cloud, and provides automatic sharding across nodes with queries executed in parallel on each CPU core.
While there are many similarities with VoltDB, the diagram above illustrates a key difference. MemSQL attempts to balance conflicting demands of real-time transactions, with data warehouse-style historical data processing. To achieve this, MemSQL organizes data in-memory as a row store, backed by a column-oriented disk store to combine real-time (recent) data with historical results.
This places it firmly in the OLTP and data warehouse space, although both solutions target the real-time data ingestion and analytics market.
Which Applications Need NewSQL Technology?
Any application that requires very high ingest rates and fast response times (average 1-2 milliseconds) but also demands transactional accuracy provided by ACID guarantees — for example, involving customer billing — need NewSQL technology.
Typical applications include:
- Real-time authorization: For example, to validate, record, and authorize mobile phone calls for analysis and billing purposes. Typically, 99.999% of database operations must complete within 50 milliseconds.
- Real-time fraud detection: Used to complete complex analytic queries to accurately determine the likelihood of fraud before the transaction is authorized.
- Gaming analytics: Used to dynamically modify gaming difficulty in real-time based on ability and typical customer behavior. The aim is to retain existing customers and convert others from free to paying players. One client was able to increase customer spend by 40% using these techniques, where speed, availability, and accuracy are critical.
- Personalized web adverts: To dynamically select personalized web-based adverts in real-time, recording the event for billing purposes, and maintaining the outcome for subsequent analysis.
While these initially may seem like edge cases compared to the majority of OLTP applications, in a 24/7 web-connected world, these present the new frontier for real-time analytics, and with the advent of the Internet of Things, there's a massive opportunity.
Although Hadoop is more closely associated with big data and has received the huge attention as of late, database technology is the cornerstone of any IT system.
Likewise, NoSQL databases appear to provide a fast, scalable alternative to the relational database, but despite the lure of licence-free open-source databases, it really does seem you get what you pay for: consistency. Finally, as VoltDB demonstrates, it may actually be cheaper than the NoSQL alternatives in the long run.
In conclusion, if you have a web-scale, OLTP and/or real-time analytics requirement, the NewSQL class of databases need serious consideration.
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