Issue-101, Design Documentation

docs/design.md

@@ -0,0 +1,149 @@
+BRRO Compressor
+--
+
+BRRO Compressor is designed to compress WavBRRO formatted time-series data. It provides such compression algorithms as:
+1. Auto
+2. Noop
+3. Fast Fourier Transform (FFT)
+4. Polynomial
+5. Constant 
+6. Idw
+
+If compressor is set to **auto** then BRRO compressor will decide which of the algorithm it should use to compress
+some special data chunk.
+
+Compressor usage:
+
+```
+Usage: brro-compressor [OPTIONS] <INPUT>
+
+Arguments:
+  <INPUT>  input file
+
+Options:
+      --compressor <COMPRESSOR>
+          Select a compressor, default is auto [default: auto] [possible values: auto, noop, fft, constant, polynomial, idw]
+  -e, --error <ERROR>
+          Sets the maximum allowed error for the compressed data, must be between 0 and 50. Default is 5 (5%). 0 is lossless compression 50 will do a median filter on the data. In between will pick optimize for the error [default: 5]
+  -u
+          Uncompresses the input file/directory
+  -c, --compression-selection-sample-level <COMPRESSION_SELECTION_SAMPLE_LEVEL>
+          Samples the input data instead of using all the data for selecting the optimal compressor. Only impacts speed, might or not increased compression ratio. For best results use 0 (default). Only works when compression = Auto. 0 will use all the data (slowest) 6 will sample 128 data points (fastest) [default: 0]
+      --verbose
+          Verbose output, dumps everysample in the input file (for compression) and in the ouput file (for decompression)
+  -h, --help
+          Print help
+  -V, --version
+          Print version
+```
+
+CSV Compressor
+--
+
+CSV Compressor allows compressing CSV formatted time-series data. It leverages BRRO Compressor functionalities to compress
+data.
+
+Compression flow:
+1. Reads provided time-series data as a CSV
+2. Transforms values of time-series data into WavBRRO and generate VSRI
+3. Compresses achieved WavBRRO
+
+Decompression flow:
+1. Read compressed WavBRRO (we call it **bro**)
+2. Decompresses data
+3. Read VSRI and retrieves timestamps
+4. Outputs time-series data as CSV
+
+In the current state in only generates a **single** WavBRRO, BRO and VSRI files which contain time-series data.
+
+CSV Compressor usage:
+
+```
+Usage: csv-compressor [OPTIONS] <INPUT>
+
+Arguments:
+  <INPUT>  Path to input
+
+Options:
+  -o, --output <OUTPUT>
+          Defines where the result will be stored
+  -u
+          Defines if we should uncompress input
+      --no-compression
+
+      --output-vsri
+          Enables output of generated VSRI
+      --output-wavbrro
+          Enables output of generated WavBrro
+      --output-csv
+          Enable output result of decompression in CSV format
+      --compressor <COMPRESSOR>
+          Select a compressor, default is auto [default: auto] [possible values: auto, noop, fft, constant, polynomial, idw]
+  -e, --error <ERROR>
+          Sets the maximum allowed error for the compressed data, must be between 0 and 50. Default is 5 (5%). 0 is lossless compression 50 will do a median filter on the data. In between will pick optimize for the error [default: 5]
+  -c, --compression-selection-sample-level <COMPRESSION_SELECTION_SAMPLE_LEVEL>
+          Samples the input data instead of using all the data for selecting the optimal compressor. Only impacts speed, might or not increased compression ratio. For best results use 0 (default). Only works when compression = Auto. 0 will use all the data (slowest) 6 will sample 128 data points (fastest) [default: 0]
+  -h, --help
+          Print help
+  -V, --version
+          Print version
+```
+
+WavBRRO
+--
+WavBRRO lib crate contains an implementation of WavBRRO format. The format is a based on the WAV format to be used to 
+store raw time-series data.
+
+For more details on WavBRRO you may follow [here](../wavbrro/README.md).
+
+Vsri
+--
+Vsri lib crate contains an implementation of the VSRI (Very Small Rolo Index). The index is made for detection of gaps 
+in continuous data with the same sampling rate.
+
+Each continuous segment of data will be mapped to a line using the formula y = mx + B plus the number of points in 
+the data series, where:
+- m - Sampling rate
+- b - Series initial point in time in [x,y]
+- x - sample # in the data file, this is ALWAYS sequential. There are no holes in samples
+- y - time
+
+This way, discovering the segment number is solving the above equation for X if the time provided is bigger than 
+the initial point.
+
+Index structure:
+1. index_name: Name of the index file we are indexing
+2. min_ts: the minimum TS available in this file
+3. max_ts: the highest TS available in this file
+4. vsri_segments: Description of each segment:
+    1. Sampling rate
+    2. initial sample position X0
+    3. initial sample timestamp Y0
+    4. Number of samples in the segment
+
+Example of content of an index:
+
+     55745
+     59435
+     15,0,55745,166
+     15,166,58505,63
+
+Where:
+
+- 55745 - min_ts
+- 59435 - max_ts
+- 15,0,55745,166 - the first segment:
+    1. 15 - Sampling rate
+    2. 0 - initial sample position for the segment X0
+    3. 55745 - initial sample timestamp for the segment Y0
+    4. 166 - the number of sampels in the first segment
+- 15,166,58505,63 - the second segment:
+    1. 15 - Sampling rate
+    2. 166 - initial sample position for the segment X0
+    3. 58505 - initial sample timestamp for the segment Y0
+    4. 63 - the number of sampels in the first segment
+
+BRRO
+--
+
+For more details on BRRO, including what it is and the concept behind it, you can refer to this [paper](../paper/BRRO.md).