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https://github.com/bitly/data_hacks

Command line utilities for data analysis
https://github.com/bitly/data_hacks

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Command line utilities for data analysis

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data_hacks

==========



Command line utilities for data analysis



Installing: `pip install data_hacks`



Installing from github `pip install -e git://github.com/bitly/data_hacks.git#egg=data_hacks`



Installing from source `python setup.py install`



data_hacks are friendly. Ask them for usage information with `--help`



histogram.py

------------



A utility that parses input data points and outputs a text histogram



Example:



    $ cat /tmp/data | histogram.py --percentage --max=1000 --min=0

    # NumSamples = 60; Min = 0.00; Max = 1000.00

    # 1 value outside of min/max

    # Mean = 332.666667; Variance = 471056.055556; SD = 686.335236; Median 191.000000

    # each ∎ represents a count of 1

        0.0000 -   100.0000 [    28]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎ (46.67%)

      100.0000 -   200.0000 [     2]: ∎∎ (3.33%)

      200.0000 -   300.0000 [     2]: ∎∎ (3.33%)

      300.0000 -   400.0000 [     8]: ∎∎∎∎∎∎∎∎ (13.33%)

      400.0000 -   500.0000 [     8]: ∎∎∎∎∎∎∎∎ (13.33%)

      500.0000 -   600.0000 [     7]: ∎∎∎∎∎∎∎ (11.67%)

      600.0000 -   700.0000 [     3]: ∎∎∎ (5.00%)

      700.0000 -   800.0000 [     0]:  (0.00%)

      800.0000 -   900.0000 [     1]: ∎ (1.67%)

      900.0000 -  1000.0000 [     0]:  (0.00%)



With logarithmic scale



    $ printf 'import random\nfor i in range(1000):\n print random.randint(0,10000)'|\

        python -|./data_hacks/histogram.py -l

    # NumSamples = 1000; Min = 2.00; Max = 9993.00

    # Mean = 4951.757000; Variance = 8279390.995951; SD = 2877.393090; Median 4828.000000

    # each ∎ represents a count of 6

        2.0000 -    11.7664 [     3]:

       11.7664 -    31.2991 [     0]:

       31.2991 -    70.3646 [     5]:

       70.3646 -   148.4956 [    11]: ∎

      148.4956 -   304.7576 [    15]: ∎∎

      304.7576 -   617.2815 [    35]: ∎∎∎∎∎

      617.2815 -  1242.3294 [    51]: ∎∎∎∎∎∎∎∎

     1242.3294 -  2492.4252 [   128]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎

     2492.4252 -  4992.6168 [   269]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎

     4992.6168 -  9993.0000 [   483]: ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎



ninety_five_percent.py

----------------------



A utility script that takes a stream of decimal values and outputs the 95% time.



This is useful for finding the 95% response time from access logs.



Example (assuming response time is the last column in your access log):



    $ awk '{print $NF}' /path/to/access.log | ninety_five_percent.py

    

sample.py

---------



Filter a stream to a random sub-sample of the stream



Example:



    $ cat access.log | sample.py 10% | post_process.py



run_for.py

----------



Pass through data for a specified amount of time



Example:



    $ tail -f access.log | run_for.py 10s | post_process.py



bar_chart.py

------------



Generate an ascii bar chart for input data (this is like a visualization of `uniq -c`)



    $ cat data | bar_chart.py

    # each ∎ represents a count of 1. total 63

    14:40 [    49] ∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎

    14:41 [    14] ∎∎∎∎∎∎∎∎∎∎∎∎∎∎



`bar_chart.py` and `histogram.py` also support ingesting pre-aggregated values. Simply provide a two column input of `countvalue` for `-a` or `valuecount` for `-A`:



    $ sort /path/to/data | uniq -c | bar_chart.py -a



This is very convenient if you pull data out, say Hadoop or MySQL already aggregated.