Activating the free Let’s Encrypt SSL certificate for your WordPress site

WPBeginner has a easy to follow tutorial on activating the free Let’s Encrypt SSL certificate for your WordPress site, assuming your web host supports them. The really simple ssl plugin mentioned makes the process painless for existing sites.

If you are on HawkHost, you can view their announcements here on their Let’s Encrypt and 2FA support. In short, you can get the free SSL certificate by:

  1. Logging to cPanel, locate the Security section and click on “Lets Encryptâ„¢ SSL” option to start the process.
  2. You can then select the domain that you would like HawkHost to issue the SSL certificate to.
  3. You will be brought to a confirmation page. On confirmation, HawkHost will generate the SSL certificate for you.
  4. Back in your WordPress admin page, activate the really simple ssl plugin and click on the button “Go ahead, activate SSL!”.
  5. You might be logged out of the admin page. If so, login again and you will discover that your WordPress site is now on SSL.
  6. Refer back to the WPBeginner tutorial for additional details like Google Analytics updates.

Find version of python package installed

Below are 3 methods we can try to find the version of an installed python package. We shall use scipy as an example.

Using pip

Method 1 – For pip 1.3 and above: pip show scipy

Method 2 – Alternative (works with older versions of pip): pip freeze | grep scipy

Using version attribute

Method 3 – Launch python/ipython, then execute the commands below:

import scipy
scipy.__version__

Reference for Method 1 is here. Reference for Method 2 is here.

Running Apache Spark with sparklyr and R in Windows

RStudio recently released the sparklyr package that allows users to connect to Apache Spark instances from R. In addition, this package offers dplyr integration, allowing you to utilize Spark as you use dplyr functions like filter and select, which is very convenient. The package will also assist you in downloading and installing Apache Spark if it is a fresh install. This post covers the local install of Apache Spark via sparklyr and RStudio in Windows 10.

As per the guide, install the latest preview release of RStudio and run the following commands to install sparklyr

install.packages("devtools")
devtools::install_github("rstudio/sparklyr")

Once installed, you should see a new tab beside Environment and History tabs in RStudio – the Spark tab. You will be able to see a “New Connection” button.

On clicking the button, you will be able to set various options from Spark and Hadoop versions (at time of writing – Spark 1.6.2 and Hadoop 2.6) to connecting to local or remote Spark clusters and whether to use dplyr as the DB interface. Let’s use the defaults here.

If this is a fresh install, RStudio will prompt a confirmation dialog box:

Upon clicking “Install”, RStudio will then proceed to download and install Apache Spark for you and attempt to connect to the local Spark instance.

At this stage you might receive a similar error message below:

Error in start_shell(scon, list(), jars, packages) : 
  Failed to launch Spark shell. Ports file does not exist.
    Path: C:\Users\<USERNAME>\AppData\Local\rstudio\spark\Cache\spark-1.6.2-bin-hadoop2.6\bin\spark-submit.cmd
    Parameters: --packages "com.databricks:spark-csv_2.11:1.3.0,com.amazonaws:aws-java-sdk-pom:1.10.34" --jars "<PATH TO R PACKAGES>\R\win-library\3.2\sparklyr\java\rspark_utils.jar"  sparkr-shell D:\Temp\RtmpO0cLos\file23c0703c73bf.out

In addition: Warning message:
running command '"C:\Users\<USERNAME>\AppData\Local\rstudio\spark\Cache\spark-1.6.2-bin-hadoop2.6\bin\spark-submit.cmd" --packages "com.databricks:spark-csv_2.11:1.3.0,com.amazonaws:aws-java-sdk-pom:1.10.34" --jars "<PATH TO R PACKAGES>\R\win-library\3.2\sparklyr\java\rspark_utils.jar"  sparkr-shell <PATH TO TEMP DIRECTORY>\Temp\RtmpO0cLos\file23c0703c73bf.out' had status 127 

If you meet this error, it may be due to Windows security permissions of the .CMD files for Apache Spark. To resolve this issue, go to the Apache Spark install directory, which should be C:\Users\<USERNAME>\AppData\Local\rstudio\spark\Cache\spark-1.6.2-bin-hadoop2.6\bin\. You should be able to see the files ending with extension .cmd. At the time of writing, the files include

beeline.cmd
load-spark-env.cmd
pyspark.cmd
pyspark2.cmd
run-example.cmd
run-example2.cmd
spark-class.cmd
spark-class2.cmd
spark-shell.cmd
spark-shell2.cmd
spark-submit.cmd
spark-submit2.cmd
sparkR.cmd
sparkR2.cmd

For each of the these .CMD files, edit the security permission for your USERNAME to allow “Read & execute” as shown below:

After editing the permissions, when you attempt to connect to Spark by running the commands below, you should not experience any more errors.

> library(sparklyr)
> library(dplyr)
> sc <- spark_connect(master = "local")

To verify, you can try out the examples from the RStudio guide, or try the adapted example below:

> iris_tbl <- copy_to(sc, iris)
> iris_tbl
Source:   query [?? x 5]
Database: spark connection master=local app=sparklyr local=TRUE

   Sepal_Length Sepal_Width Petal_Length Petal_Width Species
          <dbl>       <dbl>        <dbl>       <dbl>   <chr>
1           5.1         3.5          1.4         0.2  setosa
2           4.9         3.0          1.4         0.2  setosa
3           4.7         3.2          1.3         0.2  setosa
4           4.6         3.1          1.5         0.2  setosa
5           5.0         3.6          1.4         0.2  setosa
6           5.4         3.9          1.7         0.4  setosa
7           4.6         3.4          1.4         0.3  setosa
8           5.0         3.4          1.5         0.2  setosa
9           4.4         2.9          1.4         0.2  setosa
10          4.9         3.1          1.5         0.1  setosa
# ... with more rows
> 

Visually, you would be able to see the data frame in the Spark tab as well:

You can now have fun with Apache Spark in RStudio! You might also want to add the Apache Spark install directory C:\Users\<USERNAME>\AppData\Local\rstudio\spark\Cache\spark-1.6.2-bin-hadoop2.6\bin to your path so that you can run Spark shells (sparkr,pyspark and spark-shell) in the command line. For example, with PySpark, you should see a similar welcome screen as below after the initialization messages.