Changing the size of redo logs in python

I create a lot of small databases to do testing on. The trouble is that I often need to change the size of redo log files when I'm testing large transaction workloads or loading a lot of data. Now there are lots of better ways to do whats shown in the code below but this approach gave me the chance to keep brushing up my python skills and use the might cx_oracle driver. The following should never be considered anything but a nasty hack but it does save me a little bit of time i.e. don't use this on anything but a test database… Clearly the sensible way to do this is to write my own scripts to build databases.

The following code works it's way through the redo log files drops one thats inactive and then simply recreates it. It finished when it's set all of the redo to the right size.

Running the script is simply a case of running it with the parameters shown below

python ChangeRedoSize -u sys -p welcome1 -cs myserver/orclcdb --size 300

Note : the user is the sysdba of the container database if you are using the multitenant arhcitecture and the size is in Mega Bytes.

You should then see something similar to the following

Current Redo Log configuration
| Group No. | Thread No. | Sequence No. | Size (MB) | No of Members |  Status  |
|     1     |     1      |     446      | 524288000 |       1       | INACTIVE |
|     2     |     1      |     448      | 524288000 |       1       | CURRENT  |
|     3     |     1      |     447      | 524288000 |       1       |  ACTIVE  |
alter system switch logfile
alter system switch logfile
alter database drop logfile group 2
alter database add logfile group 2 size 314572800
alter system switch logfile
alter database drop logfile group 1
alter database add logfile group 1 size 314572800
alter system switch logfile
alter system switch logfile
alter system switch logfile
alter system switch logfile
alter database drop logfile group 3
alter database add logfile group 3 size 314572800
alter system switch logfile
All logs correctly sized. Finishing...
New Redo Log configuration
| Group No. | Thread No. | Sequence No. | Size (MB) | No of Members |  Status  |
|     1     |     1      |     455      | 314572800 |       1       |  ACTIVE  |
|     2     |     1      |     454      | 314572800 |       1       | INACTIVE |
|     3     |     1      |     456      | 314572800 |       1       | CURRENT  |

Interpolating data with Python


So as usual for this time of year I find myself on vacation with very little to do. So I try and find personal projects that interest me. This is usually a mixture of electronics and mucking around with software in a way that I don't usally find the time for normally. One of projects is my sensor network.

I have a number of Raspberry Pi's around my house and garden that take measurements of temperature, humidity, pressure and light. They hold the data locally and then periodically upload them to a central server (another Raspberry Pi) where they are aggregated. However for any number of reasons (usally a power failure) the raspberrypi's occasionally restart and are unable to join the network. This means that some of their data is lost. I've improved their resiliance to failure and so it's a less common occurance but it's still possible for it to happen. When this means I'm left with some ugly gaps in an otherwise perfect data set. It's not a big deal but it's pretty easy to fix. Before I begin, I acknolwedge that I'm effectively "making up" data to make graphs "prettier".

In the following code notebook I'll be using Python and Pandas to tidy up the gaps.

To start with I need to load the libraries to process the data. The important ones are included at the start of the imports. The rest from "SensorDatabaseUtilities" aren't really relevant since they are just helper classes to get data from my repository

In [75]:
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import matplotlib
import json
from import json_normalize
# The following imports are from my own Sensor Library modules and aren't really relevant
from SensorDatabaseUtilities import AggregateItem
from SensorDatabaseUtilities import SensorDatabaseUtilities
# Make sure the charts appear in this notebook and are readable
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)

The following function is used to convert a list of JSON documents (sensor readings) into a Pandas DataFrame. It then finds the minimum and maximum dates and creates a range for that period. It uses this period to find any missing dates. The heavy lifting of the function uses the reindex() function to insert new entries whilst at the same time interpolating any missing values in the dataframe. It then returns just the newly generated rows

In [76]:
def fillin_missing_data(sensor_name, results_list, algorithm='linear', order=2):
    # Turn list of json documents into a json document
    results = {"results": results_list}
    # Convert JSON into Panda Dataframe/Table
    df = json_normalize(results['results'])
    # Convert Date String to actual DateTime object
    df['Date'] = pd.to_datetime(df['Date'])
    # Find the max and min of the Data Range and generate a complete range of Dates
    full_range = pd.date_range(df['Date'].min(), df['Date'].max())
    # Find the dates that aren't in the complete range
    missing_dates = full_range[~full_range.isin(df['Date'])]
    # Set the Date to be the index
    df.set_index(['Date'], inplace=True)
    # Reindex the data filling in the missing date and interpolating missing values
    if algorithm in ['spline', 'polynomial'] :
        df = df.sort_index().reindex(full_range).interpolate(method=algorithm, order=order)
    elif algorithm in ['ffill', 'bfill']:
        df = df.sort_index().reindex(full_range, method=algorithm)
        df = df.sort_index().reindex(full_range).interpolate(method=algorithm)
    # Find the dates in original data set that have been added
    new_dates = df[df.index.isin(missing_dates)]
    # Create new aggregate records and insert them into the database
    # new_dates.apply(gen_json,axis=1, args=[sensor_name])
    return new_dates

This function simply takes an array of JSON documents and converts them into a DataFrame using the Pandas json_normalize function. It provides us with the dataset that contains missing data i.e. an incomplete data set.

In [77]:
def json_to_dataframe(results_list):
    # Turn list of json documents into a json dodument
    results = {"results": results_list}
    # Convert JSON into Panda Dataframe/Table
    df = json_normalize(results['results'])
    return df

The first step is to pull the data from the database. I'm using some helper functions to do this for me. I've also selected a date range where I know I have a problem.

In [92]:
utils = SensorDatabaseUtilities('raspberrypi', 'localhost')
data = utils.getRangeData('20-jan-2015', '10-feb-2015')
# The following isn't need in the code but is included just to show the structure of the JSON Record
'{"Date": "2015-01-20 00:00:00", "AverageHumidity": 35.6, "AverageTemperature": 18.96, "AveragePressure": 99838.78, "AverageLight": 119.38}'

Next simply convert the list of JSON records into a Pandas DataFrame and set it's index to the "Date" Column. NOTE : Only the first 5 records are shown

In [93]:
incomplete_data = json_to_dataframe(data)
# Find the range of the data and build a series with all dates for that range 
full_range = pd.date_range(incomplete_data['Date'].min(), incomplete_data['Date'].max())
incomplete_data['Date'] = pd.to_datetime(incomplete_data['Date'])
incomplete_data.set_index(['Date'], inplace=True)
# Show the structure of the data set when converted into a DataFrame
AverageHumidity AverageLight AveragePressure AverageTemperature
2015-01-20 35.60 119.38 99838.78 18.96
2015-01-21 38.77 63.65 99617.15 19.48
2015-01-22 37.45 143.00 100909.08 20.08
2015-01-23 35.52 119.87 101306.30 20.12
2015-01-24 39.72 92.43 101528.54 19.90

The following step isn't needed but simply shows the problem we have. In this instance we are missing the days for Janurary 26th 2015 to Janurary 30th 2015

In [94]:
#incomplete_data.set_index(['Date'], inplace=True)
problem_data = incomplete_data.sort_index().reindex(full_range)
axis = problem_data['AverageTemperature'].plot(kind='bar')

Pandas offers you a number of approaches for interpolating the missing data in a series. They range from the simple method of backfilling or forward filling values to the more powerful approaches of methods such as "linear", "quadratic" and "cubic" all the way through to the more sophisticated approaches of "pchip", "spline" and "polynomial". Each approach has its benefits and disadvantages. Rather than talk through each it's much simpler to show you the effect of each interpolation on the data. I've used a line graph rather than a bar graph to allow me to show all of the approaches on a single graph.

In [95]:
interpolation_algorithms = ['linear', 'quadratic', 'cubic', 'spline', 'polynomial', 'pchip', 'ffill', 'bfill']

fig, ax = plt.subplots()
for ia in interpolation_algorithms:
    new_df = pd.concat([incomplete_data, fillin_missing_data('raspberrypi', data, ia)])
    ax = new_df['AverageTemperature'].plot()

handles, not_needed = ax.get_legend_handles_labels()
ax.legend(handles, interpolation_algorithms, loc='best')

Looking at the graph it appears that either pchip (Piecewise Cubic Hermite Interpolating Polynomial) or Cubic interpolation is going to provide the best approximation for the missing values in my data set. This is largely subjective because these are "made up values" but I believe either of these approaches provide values that are closest to what the data could have been.

The next step is to apply one to the incomplete data set and store it back in the database

In [96]:
complete_data = pd.concat([incomplete_data, fillin_missing_data('raspberrypi', data, 'pchip')])

axis = complete_data.sort_index()['AverageTemperature'].plot(kind='bar')

And thats it. I've made the code much more verbose that it needed to be purely to demonstrate the point. Pandas makes it very simple to patch up a data set.


Java Version Performance

Sometimes it’s easy to loose track of the various version numbers for software as they continue their march ever onwards. However as I continue my plans to migrate onto Java8 and all of the coding goodness that lies within I thought it was a sensible to check what difference it would make to swingbench in terms of performance.

Now before we go any further it’s worth pointing out this was a trivial test and my results might not be representative of what you or anyone else might find.

My environment was

iMac (Retina 5K, 27-inch, Late 2014), 4 GHz Intel Core i7, 32 GB 1600 MHz DDR3 with a 500GB SSD

Oracle Database 12c ( with the January Patch Bundle running in a VM with 8GB of memory.

The test is pretty simple but might have an impact on your choice of JVM when generating a lot of data (TB+) with swingbench. I simply created a 1GB SOE with the oewizard. This is a pretty CPU intensive operation for the entire stack : swingbench, the jdbc drivers and the database. The part of the operation that should be most effected by the performance of the JVM is the “Data Generation Step”.

So enough talk what impact did the JVM version have?


Now the numbers might not be earth shattering but it’s nice to know a simple upgrade of the JVM can result in nearly a 25% improvement in performance of a CPU/database intensive batch job. I expect these numbers to go up as I optimise some of the logic to take advantage of Java8 specific functionality.


PDF Generation of report files

I finally got round to adding some code that creates pdf files such that you can convert the “XML” result files into something more readable. However this new functionality requires a Java8 VM to work. You can download the latest build here.

All you need to do is to run swingbench and from the menu save the summary results.


Minibench and charbench will automatically create a results config file in the local directory after a benchmark run. The file that’s created will typically start with “result” and it should look something like this.

[bin]$ ls ccwizard.xml coordinator oewizard shwizard.xmlbmcompare charbench data oewizard.xml swingbenchccconfig.xml clusteroverview debug.log results.xml swingconfig.xmlccwizard clusteroverview.xml minibench shwizardAll you need to do after this is to run the “results2pdf command

[bin]$ ./results2pdf -c results2pdf

There’s really only 2 command line options

[bin]$ ./results2pdf -husage: parameters: -c the config file to convert from xml to pdf -debug send debug information to stdout -h,--help print this message -o output filenameThey are for the input file (-c) and the output file (-o).

The resultant file will contain tables and graphs. The type of data will depend heavily on the type of stats collected in the benchmark. For the richest collection you should enable

  • Full stats collection
  • Database statistics collection
  • CPU collection

An example of the output can be found here.


I plan to try and have the resultant pdf generated and displayed at the end of every bench mark. I’ll include this functionality in a future build.