Big Data Benchmarks: Toward Real-Life Use Cases

The Transaction Processing Council (TPC), working with Cloudera, recently announced the new TPCx-HS benchmark, a good first step toward providing a Big Data benchmark.

In this interview by Roberto Zicari with Francois Raab, the original author of the TPC-C Benchmark, and Yanpei Chen, a Performance Engineer at Cloudera, the interviewees share their thoughts on the next step for benchmarks that reflect real-world use cases.

This interview was originally published at ODBMS.org; thanks to Roberto for his permission to republish.


There have been a number of attempts at constructing big data benchmarks. None of them has yet gained wide recognition and usage. Why?

Yanpei: Many big data benchmarks are just like big data systems – new, and with room to improve and grow. In more detail big data systems: – rapidly evolve, so it’s important to define performance in ways that matter for end customers. – consist of many interdependent components, so it’s difficult to measure performance in a reliable fashion. – service diverse business needs using diverse implementations, so benchmarks need to accommodate different system implementations.

Francois: It’s unlikely that a big data benchmark will gain wide recognition until a clear “playing field” has emerged and focused the competitive pressure. There are three phases in the evolution of a new technology. First, the technology is introduced and applied to a wide array of solutions without a proven return on investment. Next, a “killer app” emerges from the early adopters and its rapid growth draws all the vendors into competing on a common playing field. Lastly, some technologies emerge as clear winners in the race and the market start to consolidate around a few dominant vendors. Big data has not entered the second phase yet.

Is it possible to build a truly representative big data benchmark?

Yanpei: Absolutely! To me, the rise of “big data” in part comes from our increased ability to instrument, measure, and ultimately derive value from large scale systems – technology systems, financial systems, medical systems, or physical systems touching day-to-day life. Big data systems, as a special case of technology systems, also deal with ever increasing instrumentation and measurement. Over time, I am absolutely confident that we will increase our understanding of big data systems, and with it, improve the quality of our big data benchmarks.

Cloudera’s broad customers base gives us visibility into big data deployments across telecom, banking, retail, manufacturing, media, government, healthcare, and many other industry sectors. We’re in a great position to identify representative use cases. [Editor's note: Cloudera is actively working with the TPC on Big Data benchmarks that include SQL-on-Hadoop and other compute frameworks.]

Francois: A benchmark is a somewhat abstract (i.e. simplified) model of a real life scenario. The question we face today is to identify a scenario that Fortune 500 companies would widely recognize as relevant to their operations and vital to their competitive survival. Once that critical mass has been reached it will quickly spread to the entire commercial data processing landscape and a successful big data benchmark will be built based on that scenario.

How would you define a Big Data Benchmark ?

Yanpei: The key properties of good big data benchmarks are a re-cast of the same properties for benchmarks of more established systems.

A good big data benchmark should be representative of real-life use cases; it should generate performance insights immediately relevant to diverse and evolving big data use cases. The benchmark should also be scalable; it should stress big data systems today, as well as the vastly improved systems in the future. The benchmark should be portable, meaning it should accommodate systems with different implementations that achieve the same end-goal. The benchmark should also be verifiable, in that the results can be checked by independent auditors if needed, and end-users can reproduce on their own systems the winning configurations and result.

Can you give some examples of Successful Benchmarks ?

Yanpei: My co-author Francois was a lead contributor to TPC-C, a very successful benchmark for online transactional processing (OLTP). He can share other examples.

Francois: The success of a benchmark can be measured by its number of published results and by its longevity over shifts in the underlying technologies. By that measure TPC-C and TPC-H are leading the field. While it can be argued that they have lost relevance over their two decades lifetime, they still encapsulate critical elements at the core of the application domains they represent (transaction processing and decision support).

One of the main purposes of a benchmark is to evaluate and contrast the merits of various implementations of the same set of requirements. How do you do this with Big Data?

Yanpei: You construct benchmarks that are portable. In other words, you specify implementation-independent requirements.

Best illustrated by example – TPC-C. TPC-C specifies five operations – New Order, Payment, Delivery, Order-Status, and Stock-Level. It also describes the interdependencies between these operations. For example, every New Order will be accompanied by Payment, but only one in ten New Orders will trigger an Order Status. TPC-C describes the load that the system under test should handle – many concurrent operations arriving in randomized order with randomized inter-arrival time, but at controlled relative frequencies. TPC-C also specifies the initial content of all the datasets, as well as how the content grows over the execution of the benchmark. This is an implementation-independent set of requirements – “handle these operations on these data sets.” The underlying system could be a relational database, or a key-value store like HBase.

Francois: Benchmarks can be defined one of two ways: by creating a kit to be deployed on technology specific platforms or by specifying a set of technology agnostic requirements to be implemented at will. Because big data has first emerged from the MapReduce paradigm, we have seen a number of technology centric benchmarks (also called component benchmarks) that put a narrow focus on one or more components of a predefined solution. But we should soon expect to see a big data application emerge as the new must-have in commercial data centers.

In a recent position paper you argued for building future big data benchmarks using what you call a “functional workload model.” What is it?

Francois: We introduced a couple of terms in that position paper to highlight the core concepts underlying representative, scalable, portable, and verifiable big data benchmarks.

The “functional workload model” is a way to specify such benchmarks. It contains three things – the “functions of abstraction,” the load pattern serviced by the system, and the data sets being acted upon.

“Functions of abstraction” describes “what is being computed” without specifying “how the computation should be done.” The intent is an abstract, functional description that allows the benchmark to be portable across systems of different compute paradigms. “What is being computed” should be justified by empirical evidence, either system traces or industry-wide surveys, with emphasis on identifying the common computation goals.

The load pattern describes “what is the serviced load” without specifying “how it is serviced.” It outlines the execution frequency, distribution, arrival rate, bursts and averages over time of each individual function of abstraction.

The data sets describe “what is the data and the relationships within the data” without specifying “how it is represented.” It is in terms of the structure and interdependence between data elements, initial size and contents, how it evolves over the course of the workload execution, and how it is expected to scale with the system size and load volume.

These concepts help us routinely identify shortcomings in haphazardly specified benchmarks. For example, some of the most often-cited big data benchmarks contain artificial functions of abstraction that do not match any common use cases. Or, a multi-job, multi-query load pattern is missing altogether, or the data sets are represented in unrealistic formats that inflate performance advantages.

Why did you select TPC-C as a starting point for your work?

Yanpei: Because TPC-C already has a functional workload model within its specification. And because Francois wrote TPC-C.

Francois: The functional workload model is the underlying structure on which TPC-C was built. Subsequent TPC benchmarks, like TPC-H and TPC-E, were also built based on a functional workload model.

How does your functional workload model compares with TPC-C ?

Yanpei: TPC-C already uses the functional workload concept.

For your functions of abstractions concept to be useful, it must be applicable to different types of big data systems. Two important examples are relational databases and MapReduce. How do you do that? How does your work compare with other MapReduce-Specific Benchmarks ?

Yanpei: Best illustrated by example.

Suppose we discover that sorting data is a common operation in real-life production use cases. We would then define “sort” as a function of abstraction. We would define it in the same fashion as the official Sort Benchmark – the input data is of size X, format Y, and the system is asked to produce output sorted by order Z.

A relational database implementation could do, say, “insert into TABLE … ” followed by “select * from TABLE ordered by COLUMN”. A MapReduce implementation would use the IdentityMapper and IdentityReducer, and rely on the implicit shuffle-sort in MapReduce.

This is obvious for sort, because the sort operation has traditionally been defined in a system-independent way. In contrast, many of the existing MapReduce and relational database performance measurement tools are specified in ways that do not translate across different types of systems. The many SQL-on-Hadoop systems are fast removing the boundary. The functions of abstraction concept allows us to understand use-case at a level above than any SQL-only or Hadoop-only specifications.

What are in your opinion the Emerging Big Data Application Domains?

Francois: Everyone wants to figure out which application domain will become the big data killer app. Today, no commercial data center can live without on-line transaction processing or without decision support systems. Which big data application will become indispensable tomorrow? That is the million dollar question! Once we know that, a standard big data benchmark will soon follow.

Yanpei: The maturation of the Hadoop platform has been relentless. Its role has changed as the platform has gotten more secure, more reliable, more powerful, and (especially) more real-time. It’s no longer a system used for just big batch jobs. Instead, it has become the first place that data lands. It scales and it can store anything – no data need be discarded. It’s used to pre-process data before delivering it to an enterprise data warehouse, a document repository, an analytic engine, a CRM or ERP application, or other specialized system. Most significantly, it has begun to take over some of the work previously done by those traditional platforms, because it can do real-time search and analysis on the data directly, in place, and without further Extract-Transform-Load (ETL).

This leads to the emergence of the enterprise data hub (EDH), a new architecture to complement existing investments and help put data at the center of an organisation’s business. An enterprise data hub allows storage of any amount and type of data, for as long as is needed, and accessible in any way needed. Additional necessary attributes of EDHs include: It’s Secure and Compliant, offering perimeter security and encryption, plus fine-grained (row and column-level), role-based access controls over data, just like a data warehouse. It’s Governed, enabling users to do data discovery, data auditing, and data lineage, thus understanding what data is in their EDH and how the data are used. It’s Unified and Manageable, providing native high-availability, fault-tolerance, self-healing storage, automated replication, and disaster recovery, as well as advanced workload management capabilities to enable multiple speciacialist systems to analyze the same data set. And it’s Open, ensuring that customers are not locked in to any particular vendor’s license agreement, that you can choose what tools to use with your EDH, and nobody can hold your data or applications hostage.

The emergence of EDHs pose both challenges and opportunities for defining big data benchmarks. As Francois alluded to, the representative scenarios typically involve application domains whose performance has traditionally been measured separately, such as the case for on-line transaction processing and decision support systems. How to define and measure performance for such concurrent application domains present both a challenge and an opportunity. Further, to compare different EDHs, it becomes necessary to quantify characteristics that are previously yes/no checks – which is the more secure EDH? the better governed? the more unified and more manageable? the more open? How to quantify such characteristics will stretch our performance thinking and measurement methodology into new territory.

Future work ?

Yanpei: We have a Performance Engineering Team at Cloudera. We insist on systematic, fair, and repeatable tests both for our internal performance assessment and competitive studies. We are also engaged with community efforts to define big data benchmarks. Look for our future posts on Cloudera Engineering’s Blog!

Further Reading:

Francois Raab is a recognized, award winning expert in the field of performance engineering, benchmark design and system testing. He is the original author of the TPC-C Benchmark, the most successful industry standard measure of OLTP performance. He was also co-author of The Benchmark Handbook (pub. Morgan Kaufmann). He is currently the President of InfoSizing, Inc.

Yanpei Chen is a member of the Performance Engineering Team at Cloudera, where he works on internal and competitive performance measurement and optimization. His work touches upon multiple interconnected computation frameworks, including Cloudera Search, Impala, Apache Hadoop, Apache HBase, and Apache Hive. He is the lead author of the Statistical Workload Injector for MapReduce (SWIM), an open source tool that allows someone to synthesize and replay MapReduce production workloads.

 

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