This is the repository of the TinSpin benchmark framework. Click here for the TinSpin spatial index collection.
TinSpin is a framework for benchmarking in-memory spatial indexes.
TinSpin provides several dataset generators for point data and rectangle data. The datasets can be scaled with size and dimensionality. Each index can be tested with various loads, such as insertion, window queries, exact match queries, nearest neighbor queries, updates (moving objects) and removal.
The framework was originally developed at ETH Zurich in the GlobIS group. It is now maintained by Tilmann Zäschke.
If you want to reference this project, please consider referencing the PH-Tree instead, as TinSpin was originally developed to benchmark the PH-Tree and because there is currently no TinSpin publication: The PH-tree: A Space-Efficient Storage Structure and Multi-Dimensional Index, T. Zäschke, C. Zimmerli and M.C. Norrie, Proc. of the 2014 ACM SIGMOD Intl. Conf. on Management of Data.
- Major refactoring with modularization, new Maven module 'tinspin-common'
- Added new Example for custom testing
- Added (very basic!) import for HDF5 files.
- Testing: Updated Window Query generator again; Use result count only from first run, for comparability; Updated timings to use nanoSecs internally
- CHANGE: WQ timings now return time/query instead of time/result
- New logging output with ops/sec instead of time/op
- Operations without side effects (window query, point query, kNN search) are repeatedly executed until a minimum time has passed (in order to avoid problems with warm-up)
The Example class demonstrates how to test you own index.
Some results can be found in the doc folder. The file benchmark-high-dim-2018-12.ods contains benchmarks with high-dimensional datasets, such as GloVe-25, GloVe-50, SIFT-128, NYTimes-256 and MNIST-784 (see ann-benchmark).
Output File Format
The test data is written to tab-separated value files in target/logs.
There are several possible subfolders, which are be defined in the
TestManager class. The default folders are:
- dimsP: Point data scaled with dimensionality
- dimsR: Rectangle data scaled with dimensionality
- sizeP: Point data scaled with dataset size
- sizeR: Rectangle data scaled with dataset size
- sizePWQS: Point data scaled with size of query window
- sizeRWQS: Rectangle data scaled with size of query window
- column names:
Index data ...
- averaged results:
RSZ-R-AVG-3/3 ...(Index=RSZ, datatype=Rectangle, average of 3 successful runs of 3 initiated runs
RSZ-R-0 CLUSTER(5.0,0.0,null) ...(Index=RSZ, datatype=Rectangle, random seed=0 (equals test run ID)
By default, TinSpin averages three consecutive test runs into one average.
File Columns in TinSpin 1.x
- Index: Index and test descriptor, such as
RSZ-Rfor rectangle index, see above
- data: Test data descriptor, Such as
CUBE(1.0,0.0,null), see above
- dim: number of data dimensions
- bits: number of bits (deprecated), always 64
- N: dataset size (number of points or rectangles)
- calcMem: Estimated memory requirement [bytes] per entry, only PH-Tree variants
- memory: Total measured JVM memory [bytes]
- memory/n: Total measured JVM memory [bytes per entry]
Timing. Most parts of the test are executed in two runs or more, each run consisting of a predefined number of execution. For example, each exact match run consists of 100,000 exact match queries as defined in the
Except for load/unload, runs are repeated until at least 2 seconds (default) have passed, this is in order to give more precise timings for very short runs.
- gen: time [ms] for dataset generation
- load: total index loading time [ms]
- load/n: average loading time [micro-s/entry]
- q1/n: window query time run #1 [micro-s/returned entry]
- q2/n: window query time run #2 [micro-s/returned entry]
- pq1/n: exact match query time run #1 [micro-s/query] (was called point query)
- pq2/n: exact match query time run #2 [micro-s/query]
- up1/n: update time run #1 [micro-s/update]
- up2/n: update time run #2 [micro-s/update]
- 1-NN1: query time run #1 [micro-s/update]
- 1-NN2: query time run #2 [micro-s/update]
- 10-NN1: query time run #1 [micro-s/update]
- 10-NN2: query time run #2 [micro-s/update]
- unload: total index unloading time [ms]
- unload/n: average removal time [micro-s/entry]
Tree statistics. The following columns contain tree statistics, such as number of nodes or depth. The meaning may differ between trees.
- nodes: Number of nodes
- postLen: PH:Average length of postfixes; R-Tree&Quadtrees: depth
- AHC: PH:Number of AHC nodes
- NT: PH:Number of NT-Nodes
- NTinternal: PH:Number of NT-subnodes in all NT-Nodes
Result statistics. The following columns give an indicator of the result returned by the first test run, even if runs are repeated if they are faster than 2 seconds (default), see above. The counts should not vary much between runs, but using the first runs allows comparing the result counts of different tree as a basic form of correctness testing.
- q1-n: Number of returned window query objects
- q2-n: Number of returned window query objects
- q1p-n: Number of found objects in point query
- q2p-n: Number of found objects in point query
- d1-1NN: Average distance of nearest neighbors
- d2-1NN: Average distance of nearest neighbors
- d1-kNN: Average of sum of distance of 10 nearest neighbors
- d2-kNN: Average of sum of distance of 10 nearest neighbors
- up1-n: Number of updated objects
- up2-n: Number of updated objects
- distCalc-n : Number of distance calculation for insert, query and deletion
- distCalc1NN-n : Number of distance calculations of 1NN queries
- distCalcKNN-n : Number of distance calculations of kNN queries
For each test part, the following column contain garbage collection statistics based on Java instrumentation. They are a good indicator, but not precise!
-s is the estimated memory [MB] freed up by GC.
-t is the estimated time in [ms] used by the GC.
- load-s: Estimated size of garbage collected memory [MB]
- load-t: Estimated runtime of garbage collector [ms]
General messages column:
- msg: General messages by index wrapper and test runner