I then changed my thought process to find the age of a 65 year old who’s birth day is today. Then I compare the DateOfBirth column to that static value.SELECT COUNT(*) FROM dbo.Person p WHERE DateOfBirth <= DATEADD(YEAR,-65,GETDATE())
With the above query I bought myself an index seek and 345 logical reads. That works out to <3% of the cost.
This is true not just for date functions, but rather is applicable to almost all scalar functions.
If you have more than one task you want to run at startup, you could include code for each task in a single stored procedure. But that’s generally bad coding practice. Go with the modular approach and create a separate stored procedure for each distinct task. With sp_procoption, you can set more than one stored procedure for automatic execution. The MSDN documentation was not clear regarding the order of execution, though. I thought there might be something similar to sp_settriggerorder, but I wasn’t able to find anything like that. I ran a test on SQL 2014 with 3 stored procedures set for automatic execution.
Dave has interesting notes on procedure run order, where these procedures need to live, and even some ideas on what you might put into startup stored procedures.
The same basic set of structures, scans against both tables, to arrive at the data. Cost estimates between the two plans are very different though, with the targeted queries having a much lower estimated cost.
Performance-wise, interestingly enough, the average execution time of the first query, only returning the 10 rows, is 157ms on average, while the query grouping directly on the SalesPersonID averages about 190ms. Now, the reads tell a slightly different story with 17428 on the generic query and 5721 on the specific query. So, maybe a server under load will see a significant performance increase. However, let’s deal with what we have in front of us and say that, at least for these tests, the catch-all GROUP BY query performs well.
Grant’s recommendation is to split this out into several procedures, and if you’re having performance problems, that’s a solid move. I’m a bit more likely to keep them (especially in warehousing reports), but it’s nice to have options.
At the core of Apache Spark is the notion of data abstraction as distributed collection of objects. This data abstraction, called Resilient Distributed Dataset (RDD), allows you to write programs that transform these distributed datasets.
RDDs are immutable distributed collection of elements of your data that can be stored in memory or disk across a cluster of machines. The data is partitioned across machines in your cluster that can be operated in parallel with a low-level API that offers transformations and actions. RDDs are fault tolerant as they track data lineage information to rebuild lost data automatically on failure.
Some of these concepts are new to Spark 2.0, but all are worth learning.
To use this post to play around with streaming data, you need an AWS account and AWS CLI configured on your machine. The entire pattern can be implemented in few simple steps:
Create an Amazon Kinesis stream.
Spin up an EMR cluster with Hadoop, Spark, and Zeppelin applications from advanced options.
Use a Simple Java producer to push random IoT events data into the Amazon Kinesis stream.
Connect to the Zeppelin notebook.
Import the Zeppelin notebook from GitHub.
Analyze and visualize the streaming data.
This is a good way of getting started with streaming data. I’ve grown quite fond of notebooks in the short time that I’ve used them, as they make it very easy for people who know what they’re doing to provide code and information to people who want to know what they’re doing.
I like to test my scripts against a variety of versions/editions and I don’t like spinning VMs up and down all the time. As for the cost; some people spend their money on golf, Polish pottery and gaming rigs. I spend mine on servers, Belgian beer and travel 😉
As you can see, I also have an old Macbook Pro with 256 SSD, 4TB HDD and 8GB RAM in the mix. It’s for photos and videos, however. And someone gave me an old silver Shuttle from like 2002, but I haven’t had the time to set it up yet.
The “cloud versus local” lab is a tough call, as both sides have their advantages and disadvantages.
Today, we learned that Polybase statistics are stored in the same way as other statistics; as far as SQL Server is concerned, they’re just more statistics built from a table (remembering that the way stats get created involves loading data into a temp table and building stats off of that temp table). We can do most of what you’d expect with these stats, but beware calling sys.dm_db_stats_properties() on Polybase stats, as they may not show up.
Also, remember that you cannot maintain, auto-create, auto-update, or otherwise modify these stats. The only way to modify Polybase stats is to drop and re-create them, and if you’re dealing with a large enough table, you might want to take a sample.
The result isn’t very surprising in retrospect, and it’s good that “stats are stats are stats” is the correct answer.
model database: New default data and log file size is 8MB and default auto-growth is 64MB. This ensures that any new database created without explicitly specifying the SIZE/FILEGROWTH parameter will have 8MB initial size for all data and log files and 64MB for auto-growth for both data and log files.
For data files, having a 64MB autogrow, aligns with 1 PFS interval (which covers a range of 8088 pages = 64MB). For log files, having a 64MB autogrow helps with sizing the initial VLFs correctly so that they can be garbage claimed (wrapped-around) without which the log can keep growing.
This is much better than the prior default of 1 MB size and 10% auto-growth. Percentage growth leads to eventual pain.
You’ll notice that when I go over the parentheses the one I’ve selected and it’s pair turn yellow, unless there isn’t a pair of course. You can also use Ctrl-] to flip between the open and close parenthesis in a pair. This can be particularly useful to make sure that you remembered a close parenthesis at the end of a subquery. In this case that last close parenthesis doesn’t have a match. Now finding out that you are missing an open parenthesis doesn’t mean you know where it’s supposed to go. But you can track the different pairs, making sure that each time you open a parenthesis you close it in the correct place. In this case it belonged right at the beginning.
FYI yellow isn’t the default (it’s a light gray). I find the default hard to see (I’m getting old) so I changed it to yellow in the options under fonts and colors.
Read the whole thing.
While event handling for .Net developers is implemented in a unified way, this is not the case for SQL Server. Event handling for SQL Server lacks the “one stop shopping” afforded to .Net developers. *If* we had access to the code base for SQL Server and wanted to handle a specific event, we could add our own code, recompile sqlservr.exe, and be on our way. But since we don’t have this ability, we use SQL Server’s run-time hooks. Consider the following:
DDL Triggers: handles Data Definition Language events (synchronously).
Event Notifications: handles a wide swath of SQL Server events via Service Broker (asynchronously).
SQL Alerts: handles the following events:
Events with a specific error number or severity level that are written to the Windows Event Log.
Events for a specific performance condition.
sp_procoption: handles the startup event by specifying a stored procedure to run when the database engine service starts.
SQL Agent jobs: handles time-based events defined by user-specified job schedules (ie daily, hourly).
This sounds like the beginning of a new series.