Newbie Gets Confused

A relatively younger developer got tasked with doing some performance tests on a lot of new code. First task was to get a lot of data ready for the new code to operate on. Two weeks went by. He was still working on the script. During that time, I had a similar task. Luckily I have some experience in this department. So I generated the data, ran some small tests, then executed the big test.

On the third week, the developer came to me for some help. He said his code was slow. So he decided to dynamically drop all the triggers on the table he was working with. He wanted to issue an ALTER TABLE command from his script, which consisted of one large PL/SQL block. Of course it was not working. I told him you can't just drop a DDL statement in the middle of that block. You can only do that for DML. However you can run the DDL inside an EXECUTE IMMEDIATE. He seemed happy.

Later the same guy came back and said he was still having problems. The error indicated that the table he was trying to access did not exist. Well I told him that the table is most likely there. But he was running the script as a different user. I thought there was a public synonym which should have let his script resolve the table name. The guy was in a hurry. So I told him he could qualify the table name with the schema name prepended. That got him a bit further.

The problems this guy was running into stemmed from some lack of knowledge. And experience is the best teacher here. He had a lot more pain. For some reason he chose the wrong type for one parameter he was passing. Then when he used the variable of wrong type, Oracle needed to convert the value, causing it to not use the index that was needed to make everything fast. Ah this should not be this hard.

New World for the Data Engineer

I read an article on how to become a data engineer. It is interesting to see all the tools you would need to know. Obviously you need to know SQL. But you should also know Python? Since everything is in the cloud these days, you should know a vendor or two (AWS or Azure). Of course there is the whole NoSQL movement along with obligatory Big Data.

It seems there are a lot of Apache products ripe for the Big Data universe. There is Apache Kafka for messaging. Hadoop lets you do jobs where stuff is stored on disk (HDFS). Spark let's you run the jobs and store results in memory. There is also a Druid I have not heard about previously that is for real time.

In addition to the article I read, there is a Hacker News follow up discussion. There the cutting edge crew says Hadoop and Spark are already legacy. Kafka, RedShift, and a host of other technologies replace them. Man, even in the database world, things move fast.

Work Smarter not Harder

We have large data sets in my current project. Every year tons more data is loaded into the system. So we only keep the majority of data for 4 years. After that, we get rid of it. There are some specific items in the data that the customer wants to keep around forever. So we identify those important records on a yearly bases, and "archive" them.


Archive in this sense means to copy the important records to another table. That other table mimics the structure of the original table. We do run into extra work when structural changes are made to the original table. We need to ensure that those changes are made to the archival table as well. This undertaking is done once a year when the archive process is run.


Right now the archive time is coming due soon. So I am working on this task. I need to identify all the changes made to the original table. Actually there are a total of 37 original tables. Who knows what kind of changes were made in the last year. How can I be sure I account for all of them? Well previously this was a manual process, reviewing the changes made during the year. Now I am turning to the data dictionary.


It is easy to find newly added columns. I just search for columns in the original table that are missing from the archive table. Little more involved when data type changed in the original table. I must find where the type change to an entirely different type, or where something like the scale or precision changed. The data dictionary knows all. Just takes a SQL query to unearth the answers.

Know What You Are Doing

An analyst scheduled a meeting to talk about a problem discovered in production this year. They changed the format of some of the root level flags stored in the database. However, the fields that depend on those flags was still getting set using the old logic. Those fields in turn drive the setting of other fields that the customer uses in their reports.

The game plan was to update the function that sets the root level flags. We also will need to correct al the data previously processed incorrectly. Easy enough. A new guy was assigned to work on the root level flags issue. And I was tasked with updating the customer facing data.

The only tricky part of my fix was that it depended on the other fix being executed first. I asked the new guy to let me know when his fix was done so I could add a dependency to it. The new guy did contact me. He asked if I knew how to do a data update. I gave him an example script. It is a template I use when we update data in tables using some other tables as source.

The new guy too ka shot at his task. He asked me to review his work. Initially I saw some SQL errors in the code. He obviously did not run this code yet. I let him know about those problems. Then I noticed something weird. He was updating data in a table based on other fields in the same table. That did not seem like how we normally do things.

Upon closer inspection, I found that the dude did not understand which tables all the columns were in. I asked him to take a step back and look at the problem from a big picture perspective. I pointed out that normally data flows from table to table, not from a table to itself. You should understand what it is you are doing before trying to construct a technical solution to a problem.

Mysterious Double Instance Hampering Performance

I study the existing code base. Confer with a colleague. Then I determine the optimal plan to change the functionality to load only a slice of all the data. Feeling good about this. Write some helper functions. Make the minimal amount of changes to existing functions. Then I am ready to test.


I run the main stored procedure. Then my VPN drops. Should not be a problem. Just run my stored procedure again. It takes forever. I start to doubt my changes. Maybe I am now keying off a column that does not have an index.


Eventually it is time to leave work. Query is still running. I leave it run to see if it will ever complete. Just as I am sitting down to eat dinner, I get a call. My queries are slowing down the whole database. WTF? Now I must point out that this is Greenplum/PostgreSQL and not Oracle.


Yeah. I can kill one query. But every body says I have two queries running in parallel bringing everything to a halt. Then it hits me. That run when my VPN dropped. My client lost the connection. However the query must have kept running. I left it run now since it will not be in contention with the second query.


Let's hope that solves the performance problems. If not, I am sure a DBA can kill my jobs for me. At least I have a solid lead on my own alleged performance problems.

Formatting Trouble

I got a call from a guy I used to work with. He was producing a new report to email the customers regularly. He knew what he wanted the report to look like. Unfortunately, the actual report was not coming out the way he wanted. He asked me for some pointers.

 

The specific problem was that the text in the report was wrapping to the next line at unexpected locations. I asked him to determine whether the tool he used to view the report was also doing some wrapping of its own. We looked at the report in binary format. We saw some carriage return characters inserted. Also saw a bunch of spaces inserted. Both contributed to his problems.

 

I have done reports using SQL before. It takes some tweaking to get your reports to line up the way you want them to. Sometimes you need to set the width for some columns in SQL*Plus (the report was spooling the output of SQL*Plus executing a SQL script). Other times you need to set some parameters related to wrapping.

 

I don’t work on that project any more. Therefore, I did not go and solve all the problems. I did review and confirm that there were no blatant problems with the SQL For formatting problems such as these, you got to dig in and understand how output is formatted by SQL*Plus. You can’t expect it to look just the way you put_line() the data.

Saved by the Notes

I attended a meeting called by the new manager. He compiled a list of things he thought we going out in the next release. He wanted to make sure all the developers were on the same page as him. There were a bunch of developers in attendance.

I had actually prepared for the meeting. I went over every item I was doing for the next release. I new my statuses. When it came to my turn, the manager asked me about my items. Then he asked about an extra item. It was not on my radar. So I said I needed some time to research it.

There were some surprises like they were taking the database down on the last week of our development schedule. So the pressure is on to deliver early. I looked up the extra item in our ticketing system. It rung a bell. A tester wrote a defect against something I pushed out in the last release.

So I went back to the original ticket that backed the work I did last time. There in the notes were all the details. I documented exactly what I had done, where I left things when I went on vacation, and the details of the progress made while I was on vacation.

Somehow the info did not get forwarded into the new ticket. This issue was a non-issue. Thankfully I write everything down into the notes section of my tickets so I don't have to remember these pesky details.