Centos7 / RHEL7 Services with SystemD + Systemctl For Dummies – Presto Example

History – SystemV & Init.d

Historically in Centos and RHEL, you would use system-v to run a service.  Basically an application (e.g. Spring Boot) would provide an init-d script and you would either place it in /etc/init.d or place a symbolic link from there to your script.

The scripts would have functions for start/stop/restart/status and they would follow some general conventions.  Then you could use “chkconfig” to turn the services on so they would start with the sysem when it rebooted.

SystemD and SystemCTL

Things have moved on a bit and now you can use SystemD instead.  It is a very nice alternative.  Basically, you put a “unit” file in /etc/systemd/system/.service.  This unit file has basic information on what type of application you are trying to run and how it works.  You can specify the working directory, etc as well.

Here is an example UNIT file for Facebook’s Presto application.  We would place this at /etc/systemd/system/presto.service.

After=syslog.target network.target

ExecStart=/opt/presto/current/bin/launcher start
ExecStop=/opt/presto/current/bin/launcher stop


Here are the important things to note about this:

  1. You specify the user the service will run as – it should have access to the actual program location.
  2. Type can be “forking” or “simple”.  Forking implies that you have specific start and stop commands to manage the service (i.e. it kind of manages itself).  Simple implies that you’re just running something like a bash script or a Java JAR that runs forever (so SystemD will just make sure to start it with the command you give and restart it if it fails).
  3. Restart=always will make sure that, as long as you had it started in the first place, it starts whenever it does.  Try it; just kill -9 your application and it will come back.
  4. The install section is critical if you want the application to start up when the computer reboots.  You can not enable it for restart without this.

Useful Commands

  • sudo systemctl status presto (or your app name) –> current status.
  • sudo systemctl stop presto
  • sudo systemctl start presto
  • sudo systemctl restart presto
  • sudo systemctl enable presto -> enable for starting on reboot of server.
  • sudo systemctl disable presto -> don’t start on reboot of server.
  • sudo systemctl is-enabled presto; echo $? –> show if it is currently enabled for start-on-boot.

My VI Cheat Sheet

For years, I’ve been somewhat avoiding learning any advanced features of VIM. I have always predominantly relied on desktop editors for anything complex and just use VI to do basic text modification.

Anyway, I’m finally trying to change that. So, I’ll start forcing myself to do things in VIM and will record the keys here over time. I’m just starting with one command though; so it’ll be a while before this is useful! 🙂

My Cheat Sheet

Remember, generally you want to press “esc” before doing these.

  • Search Forward & Backwards
    • Forward = /search-term
    • Backward = ?search-term
  • Show or Hide Line Numbers
    • : set number
    • :set nonumber
  • Edit Multiple Lines (e.g. Block Comment Lines 10-20 With #)
    • :10,20s/^/#/
  • Clear Highlight After Search
    • There are some fancy ways, but just search for something that won’t exist and it will clear.  For example:
      • /blahfwoeaf


The Python yield keyword explained

I don’t usually re-blog posts, but this person’s post is a wonderful explanation of what yield does in python, and I definitely recommend reading through it.

Python Tips

Hi there folks. Again welcome to yet another useful tutorial. This is again a stackoverflow answer. This one is related to the Python yield keyword. It explains you what yield, generators and iterables are. So without wasting any time lets continue with the answer.

To understand what yield does,

View original post 1,362 more words

Postgres Schema Creation

Historically, I have not worked with Postgres much. So, when I had to start using it, one of my first questions was how to create a schema, and how to use it for my new tables and such.

Creating a schema is exactly what you expect:

create schema myschema;

But using it is not quite what I expected.  Of course, you can do the standard thing when you’re managing your objects and use . like this:

create table myschema.mytable (x int);

But what if you just want:

create table mytable (x int);

to go into myschema by default?  To do this in Postgres, you have to add the schema to your search path.  By default your search path will be just set to the public schema; you can view it like this:

SHOW search_path;

You can set it to one or more schemas in reality.  The first schema your query sees a the named table in will be the one it takes it from.  The first schema in the list will be the default one for when you create new objects too.  So, if you did this:

SET search_path TO myschema;
create table mytable (x int);

Then your table would in fact be created in the “myschema” schema properly.

Database Star Schemas and Snowflake Schemas

Schema Confusion

A lot of people very regularly work with databases (even high end ones), but get thrown by terms like star-schema, snowflake-schema, etc. due to lack of formal training or working with data warehousing technologies.

These same people will often be perfectly comfortable with indexing, query optimization, foreign keys, concepts of de-normalization and normal forms, etc.

I personally started working with the actual “Snowflake” database recently https://www.snowflake.com/about/ and had to review what a snowflake shema was when I started looking at it.

Useful Articles

I found an interesting article on Star schemas vs Snowflake schemas pretty quickly, and back tracked it to precursor articles digging into the Star and Snowflake schemas respectively.  Here are each in case you want the original content; I’m just going to paraphrase it below to give people a quick overview and/or refresher.

Star Schema

A star schema just means that your main table has a primary key made out of multiple columns, each of which is a foreign key to a “dimension” table.  Then you have one or more “fact” columns in addition to the primary key.

The dimension columns will be all the relevant attributes you may want to aggregate and/or query the main table on.  For example, you might have a table for the date which breaks out the year, month, day, and day-of-week so they can be directly used.  You may then have another dimension table for the geographical region with columns for the continent, country, and city, for example, so you can aggregate on those.

Each dimension table is NOT de-normalized though.  So, if you have “New York City” as the city for 1 million rows, you are literally repeating that a million times.  This makes queries easy to write but has a penalty in terms of data storage (which can be bad if you’re, say, in the cloud and paying more for more storage over time).

Snowflake Schema

Plain and simple; a snowflake schema is a star schema where the dimension tables are normalized.  This means that, for example, the geographical region dimension table itself would actually be turned into 4 tables (kind of its own star schema).  You would have one table for the continent, one for the country, one for the city, and one main table for the combination of the 3 as a primary key.

This makes queries more complex and possibly a little slower, but it means we have complete normalization and are not wasting any data storage.  Also, if say, a city changed its name, we would have exactly one database cell to update where as in a star schema we would have to update potentially millions of rows with copies of that name.

Why the Names?

If you think of a “Star Schema”, picture a main table with, say, 5 extra dimension tables around it like the 5 points of a star.  Makes sense, right?

Now, for a snowflake, picture each point being 5 tables by itself… so each point is its own star.  This starts to branch out like a snowflake.  Just think of fractals if you don’t believe me :).



First Steps in Microsoft Azure

As part of a new opportunity, I am lucky enough to have to take a deep dive into the Microsoft Azure cloud offering.

I’m really just getting started, but I found an awesome educational tool which is created by Microsoft itself here -> https://docs.microsoft.com/en-us/learn/.

It is basically an automated tutorial which includes embedded videos and an embedded command line interface.  So, you just read through, follow instructions, and spawn up and manipulate virtual machines (Windows or Linux) in a matter of minutes.

I just did the first level; but it was an amazingly pleasant experience.  So, if you’re learning Azure like me, I suggest starting there!  There appear to be many more modules to go and I’m expecting great things after the first one :).

VectorWise DB – Actian – Set Cores-Per-Query

The Basics

Actian Ingres Vector(wise) is very adept at parallelizing the processing of queries over multiple processor cores. You can choose how many cores to use per query in the server configuration and it can be overridden on a per-query basis using a with-clause.

How do you determine the server default for cores-per-query?

If you go to your local vectorwise distribution and change-directory to “$II_SYSTEM/ingres/data/vectorwise/”, you will see a vectorwise.conf file. Keep in mind that Actian is changing the name of Vectorwise to Vector, so I cannot promise that the file name will be exactly the same in the future. If you edit this file, can you will find the parameter “max_parallelism_level” under the engine section. The value of this setting is the maximum number of cores that your queries will use by default when executing (they may use less). I believe you most likely need to restart the server (ingstop/ingstart) in order to see changes to this value take effect.

Setting parallelism on a per-query basis

It is possible to override the parallelism of a specific query via the SQL command used to execute it (though you have to decide for yourself whether this is a good idea or not). For example, you could do: “select a.* from test_table1 a join test_table2 b on a.id = b.id with max_parallel 16;” to make your query use 16 cores instead of the configured amount.

Interestingly enough, if I do this on my server which is configured to use a maximum of 8 cores, and I set the query parallelism to 8, I get a 4x performance improvement. The server only appears to be using 2 of the 8 allowed cores when it executes by default. Telling the query to explicitly use 8 thus yields a large performance improvement. Even more interestingly, if I bump up the 8 to 16 for the query, I get some more performance improvement (around 20%) which seems to indicate that the explicit query setting can use more than what is defined as the maximum in the vectorwise.conf file.