NOSQL BENCHMARK TESTS UPDATE
Workload A: Update heavily Workload B: Read mostly Workload C: Read only Workload D: Read latest Workload E: Scan short ranges Workload F: Read-modify-write Workload G: Write heavilyġ) The number of records manipulated (read or written) 2) The number of columns per each record 3) The total size of a record or the size of each column 4) The number of threads used to load the system The performance of the system was evaluated under different workloads: In addition, the threads measure the latency and achieved throughput of their operations and report these measurements to the statistics module. The threads throttle the rate at which they generate requests, so that we may directly control the offered load against the database.
NOSQL BENCHMARK TESTS SERIES
Each thread executes a sequential series of operations by making calls to the database interface layer both to load the database (the load phase) and to execute the workload (the transaction phase). A basic operation is an action performed by the workload executor, which drives multiple client threads.
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The values in each field were random strings of ASCII characters, 100 bytes each.ĭatabase performance was defined by the speed at which a database computed basic operations. A primary key identified each record, which was a string, such as "user234123." Each field was named field0, field1, and so on. The number of records to scan is also selected randomly from the range between 1 and 100.Įach workload was targeted at a table of 100,000,000 records each record was 1,000 bytes in size and contained 10 fields. " Insert: Inserts a new record. " Update: Updates a record by replacing the value of one field. " Read: Reads a record, either one randomly selected field, or all fields. " Scan: Scans records in order, starting at a randomly selected record key. Operations against a data store were randomly selected and could be of the following types: " which operation to perform " which record to read or write
A workload was defined by different distributions assigned to the two main choices: We have measured database performance under certain types of workloads. Ï a framework with a workload generator Ï a set of workload scenarios Therefore, the databases were tested under the same conditions, regardless of their specifics.įor benchmarking, we used Yahoo Cloud Serving Benchmark, which consists of the following components: We certainly understand this, but the aim of this investigation is to determine the best use cases for different NoSQL products. We also tested MySQL Cluster and sharded MySQL, taking them as benchmarks.Īfter some of the results had been presented to the public, some observers said MongoDB should not be compared to other NoSQL databases because it is more targeted at working with memory directly. Ï Cassandra, a column family store Ï HBase (column-oriented, too) Ï MongoDB, a document-oriented database Ï Riak, a key-value store Using Amazon virtual machines to ensure verifiable results and research transparency (which also helped minimize errors due to hardware differences), we have analyzed and evaluated the following NoSQL solutions: We wanted to do independent and unbiased research to complement the work done by the folks at Yahoo.
NOSQL BENCHMARK TESTS SOFTWARE
Database vendors usually measure productivity of their products with custom hardware and software settings designed to demonstrate the advantages of their solutions. That variety makes it difficult to select the best tool for a particular case. In 2012, the number of NoSQL products reached 120-plus and the figure is still growing. They have a simple API, serve huge amounts of data and provide high throughput. NoSQL data management systems are inherently schema-free (with no obsessive complexity and a flexible data model) and eventually consistent (complying with BASE rather than ACID). Often referred to as NoSQL, non-relational databases feature elasticity and scalability in combination with a capability to store big data and work with cloud computing systems, all of which make them extremely popular. This article is our vendor-independent analysis of NoSQL databases, based on performance measured under different system workloads. IN THE NEWS: MySQL users caution against NoSQL fadĪs R&D engineers at Altoros Systems Inc., a big data specialist, we were inspired by Yahoo's endeavors and decided to add some effort of our own. Ï The research did not provide all the information we needed for our own analysis. Ï Though Cassandra, HBase, Yahoo's PNUTS, and a simple sharded MySQL implementation were analyzed, some of the databases we often work with were not covered. Ï Yahoo used high-performance hardware, while it would be more useful for most companies to see how these databases perform on average hardware.
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