pietempesti98/bloomfilter_spark

Implementation of a bloom filter using the spark framework made by Stefano Dugo, Gabriele Marino, Pietro Tempesti and Benedetta Tessa

Bloom Filter - Spark

Implementation of a Bloom Filter using the Spark framework in Python. All the documentation for this project can be found here

How to run the algorithm

Note: execute all the commands below inside the bloomfilter_spark folder

Pre-installation

In order to run the algorithm on a cluster, you have to install the same python environment on all the cluster's machines.

These are the steps to create a virtual environment with all the needed packages:

1. Only on debian/ubuntu install python venv

   sudo apt-get install python3-venv

2. create a virtual environment and activate it

   python -m venv pyspark_venv
   source pyspark_venv/bin/activate
   

3. install all the needed dependencies using pip

   pip3 install pyspark mmh3 venv-pack

4. zip the virtual environment in a .tar.gz archive

   venv-pack -o pyspark_venv.tar.gz

Spark submission

export PYSPARK_PYTHON=./environment/bin/python
spark-submit --archives pyspark_venv.tar.gz#environment main.py [input path] [output path] [false positive rate] 

spark-submit example

spark-submit --archives pyspark_venv.tar.gz#environment main.py data/data.txt output 0.01

Note: remember to delete the output folder before the next execution using hadoop fs -rm -r [output path]

The input file

We have tested the bloom filter with the title.ratings IMDb dataset (available here). The dataset is a .tsv file, with an header line containing the structure of the data:

tconst averageRating numVotes

We transform this file in a .txt file without the header.

tt0000001	5.7	1882
tt0000002	5.9	250
tt0000003	6.5	1663
tt0000004	5.8	163
tt0000005	6.2	2487
tt0000006	5.2	166
tt0000007	5.4	773
tt0000008	5.4	2024
tt0000009	5.3	194
tt0000010	6.9	6803
tt0000011	5.3	346
tt0000012	7.4	11692
tt0000013	5.7	1801
...             ...     ...

In the setup phase, in which we compute m for each bloom filter, we also pre-process this dataset, removing the numVotes column and rounding the values in the averageRating column. You can find the data.txt file (and a reduced version of the same file used for testing purposes data1.txt) in this folder.

Outputs

The algorithm generates two output folders: bloom_filters, with a bloom filter for each vote, and fp_rates, with the number of false positives and the false positive rate computed for each bloom filter.

You can find these folders inside the <output path> specified as input parameter; for example, if you specify output as output path, the output structure will be:

output
| 
|_ bloom_filters
|
|_ fp_rates

Output printed by the application

This is an example of the output printed by the execution of the application, using 0.01 as p value

***** results *****


vote 1 --> false positives: 12481, false positive rate: 0.01002832292147922

vote 2 --> false positives: 12872, false positive rate: 0.010385712383864707

vote 3 --> false positives: 12228, false positive rate: 0.009938215314994612

vote 4 --> false positives: 11999, false positive rate: 0.01002203360667924

vote 5 --> false positives: 11349, false positive rate: 0.009860438606627667

vote 6 --> false positives: 9953, false positive rate: 0.009960091585208669

vote 7 --> false positives: 9192, false positive rate: 0.010166252289397545

vote 8 --> false positives: 8837, false positive rate: 0.010116007262187402

vote 9 --> false positives: 11843, false positive rate: 0.010266826525048092

vote 10 --> false positives: 12337, false positive rate: 0.010021908963666214

The Hadoop implementation

You can find our implementation of the bloom filter using Hadoop in this repository