The provided source material details resources for obtaining and generating synthetic and real datasets for performance testing, benchmarking, and software evaluation. These datasets are used to stress-test systems, assess hardware and software performance, and ensure applications can handle realistic volumes of data. The information is not related to consumer free samples, promotional offers, or product trials for categories like beauty, baby care, or pet food. Instead, it focuses on technical data sources for database load testing, benchmarking, and quality assurance in IT and software development contexts.
Understanding Test Data for Performance Evaluation
Performance testing requires data that mimics real-world conditions to provide accurate insights into system behaviour. The sources highlight the challenges in obtaining suitable data, such as the time required to build large files, the risk of violating data privacy rules when using production data, and the complexity of test data management tools. Solutions often involve generating synthetic data that replicates the structure and statistical distributions of real data without using actual personal or business information.
Synthetic data generation tools, such as IRI RowGen, are mentioned as methods to create large volumes of structured data for testing. These tools can produce files in various formats (e.g., CSV, JSON, XML) and load them directly into databases for benchmarking. The goal is to simulate "worst-case scenario" volumes to accurately assess software performance. For example, testing with a high volume of customer records can reveal how a database handles large queries or concurrent user requests.
Sources of Sample Data for Benchmarking
Several sources of sample data are listed, which can be used for benchmarking and testing. These datasets vary in size, origin, and format, and they are often used by developers and database administrators to evaluate system performance under different conditions.
Free Sample Data for Database Load Testing
One source provides free sample data files in CSV format for database load testing. The data is described as high-quality, fake data constructed from real first and last names, with company names, street addresses, and other fields randomised. The data covers multiple countries with a roughly even distribution. Key characteristics include: - Data Types: Names, company names, addresses, city, county, state/province, ZIP/postal codes, phone and fax numbers (with correct area codes and exchanges for their location), email and web addresses. - Format: CSV files that are import-ready, with no special characters or formatting issues. - Purpose: To test software with a large volume of data, simulating real-world usage without using actual customer data. This helps identify performance bottlenecks and ensure scalability.
IRI RowGen and Voracity Platform
IRI’s RowGen tool and the Voracity data management platform are highlighted for generating synthetic test data. These tools are designed to create large datasets for system evaluation, including for database benchmarking. Key features include: - Data Generation: Can produce files in multiple formats (CSV, JSON, XML, LDIF, ASN.1, COBOL, etc.) and insert or bulk-load data into relational and NoSQL databases. - Customisation: Allows generation of any number of files or tables with over 100 data types, supporting fixed or delimited formats. It can also filter, select, and transform data to emulate production conditions. - Use Cases: Stress-testing software and hardware, performing data quality assessments, and evaluating processing paradigms. It respects the layout and relationships of production tables from existing DDL (Data Definition Language) for benchmarking database prototypes or data warehouse ETL operations.
Snowflake Sample Data Sets
Snowflake provides sample data sets, including industry-standard TPC-DS and TPC-H benchmarks, for evaluating SQL support. These are available in a shared database named SNOWFLAKESAMPLEDATA. Key points: - Access: The database is shared with the user’s account; if not visible, it can be created manually. - Cost: No storage charges are incurred, but executing queries requires a running warehouse, which consumes credits. - Purpose: To test and benchmark a broad range of Snowflake’s SQL capabilities, useful for developers and analysts working with cloud data platforms.
Other Datasets for Benchmarking
A blog post lists various sources of real or quasi-real data for benchmarking and testing. These datasets are preferred over randomly generated data because they have realistic distributions, making results more meaningful. Examples include: - Sakila Test Database: A small, fake database of movies. - Employees Test Database: A small, fake database of employees. - Wikipedia Page-View Statistics: Large, real website traffic data. - IMDB Database: A moderately large, real database of movies. - FlightStats Database: Flight on-time arrival data, easy to import into MySQL. - Bureau of Transportation Statistics: Airline on-time data, downloadable in customisable ways. - Airline On-Time Performance Data from data.gov: Similar to the above, providing data on flight delays and causes. - Statistical Review of World Energy from British Petroleum: Real data on global energy usage. - Amazon AWS Public Data Sets: A variety of data, such as the Human Genome mapping and US Census data. - Weather Underground Weather Data: Customisable and downloadable as CSV files.
These datasets are useful for testing under realistic conditions, with sizes ranging from a few megabytes to terabytes.
db-benchmarks.com
This platform aims to provide fair, transparent, and reproducible database and search engine benchmarks. It uses an open-source test framework to ensure results are consistent and easily understandable. Key aspects: - Fairness and Transparency: Clearly states the conditions under which performance is measured. - Control Over Variation: Allows control over the coefficient of variation to ensure stable results. - Reproducibility: Anyone can reproduce tests on their own hardware. - Simplicity: Uses simple charts for easy comprehension. - Extensibility: A pluggable architecture allows adding more databases to test.
The platform addresses issues in other benchmarks, such as inconsistent hardware, which can lead to unreliable results. For example, running tests on different hardware makes it difficult to compare performance percentages accurately. A coefficient of variation lower than 5% is challenging to achieve, and small differences may not be significant.
Applications in UK Context
While the sources are international, the principles apply to UK professionals in IT, software development, and data analysis. For instance, UK-based companies might use these resources to: - Test E-commerce Platforms: Ensure databases can handle high traffic during sales events like Black Friday or Boxing Day. - Benchmark Financial Systems: Assess performance of transaction processing systems under load. - Validate Data Warehouses: Test ETL processes with realistic data volumes.
The availability of free or open-source tools and datasets makes these resources accessible to UK organisations, including SMEs and startups. However, users should verify the terms of use for any dataset, especially those from public sources, to ensure compliance with UK data protection regulations like GDPR.
Considerations for UK Users
When selecting sample data for performance testing, UK professionals should consider: - Data Privacy: Even synthetic data should not contain real personal information. The provided fake data sources are designed to avoid this issue. - Relevance: Choose datasets that match the domain of the application (e.g., retail, transportation, energy) for more accurate testing. - Tool Compatibility: Ensure that the data format (e.g., CSV) is compatible with the target system, such as UK-specific database platforms or cloud services. - Cost: Some tools or datasets may have associated costs, though many listed are free or open-source.
For example, Snowflake’s sample data is free to query but incurs costs for compute resources, which should be factored into testing budgets. Similarly, while db-benchmarks.com is open-source, running tests on cloud instances like AWS may incur expenses.
Conclusion
The provided source material offers a range of resources for obtaining and generating sample data for performance testing and benchmarking. These include free CSV files for load testing, synthetic data generation tools like IRI RowGen, industry-standard benchmarks from Snowflake, and various real-world datasets for realistic testing. For UK professionals, these resources can help ensure software and databases perform reliably under load, though careful consideration of data relevance, privacy, and cost is essential. The information is technical and focused on IT applications, not consumer samples or promotional offers.