.NET SqlClient(and other ODBC drivers as well) has a built-in solution. Your application’s
connection stringhas quite a few parameters available to provide configuration and information, and one that seems to get overlooked is
Application Name. This one does exactly what it says on the tin – it lets you specify a name that will be displayed to anyone looking for it in SQL Server, including
sp_whoisactive. Anyplace you have the ability to write a connection string, you can use this. It costs you nothing!
You can also start getting fancy with resource governor as well, segmenting pools based on application name.
(2) Data Sources
From a single source such as a data warehouse. This is the most traditional path for BI development, and still has a very valid place in many BI/analytics deployments. This scenario puts the work of data integration on the ETL process into the data warehouse, which is the most appropriate place.
Directly from various systems. This can be done, but works well only in specific cases – it definitely won’t work well if there are a lot of highly normalized tables, or if there’s not a straightforward way to relate the disparate data together. Trying to go directly to the source systems & skip an intermediary data warehouse puts the “integration” burden on the data source view in Analysis Services, so plan for plenty of time testing if you’re going to try this route (i.e., it can be much harder, not easier). Note that this option only makes sense if the data is stored in Analysis Services because it needs to be related together somehow (i.e., DirectQuery mode, discussed next in #3, with > 1 data source won’t work if a user tries to combine data sources because the data is not inherently related).
If you’re thinking about Azure Analysis Services, this post is a good one.
Congratulations, you’ve configured a remote distributor, configured all of your AG replicas as publishers, and configured your SQL Database as a subscriber! Now you want to ensure that transactions are replicating to the database, and that they continue to do so if there is a failover in the AG.
Read on for the two testing scenarios.
Sunil Agarwal has a two-part series on columnstore data elimination. First up is column elimination:
Now, let us run the same query on the table with clustered columnstore index as shown in the picture below. Note, that the logical IOs for the LOB data is reduced by 3/4th for the second query as only one column needs to be fetched. You may wonder why LOB? Well, the data in each column is compressed and then is stored as BLOB. Another point to note is that the query with columnstore index runs much faster, 25x for the first query and 4x for the second query.
In the context of rowgroup elimination, let us revisit the previous example with sales data
- You may not even need partitioning to filter the rows for the current quarter as rows are inserted in the SalesDate order allowing SQL Server to pick the rowgroups that contain the rows for the requested date range.
- If you need to filter the data for a specific region within a quarter, you can partition the columnstore index at quarterly boundary and then load the data into each partition after sorting on the region. If the incoming data is not sorted on region, you can follow the steps (a) switch out the partition into a staging table T1 (b) drop the clustered columnstore index (CCI) on the T1 and create clustered btree index on T1 on column ‘region’ to order the data (c) now create the CCI while dropping the existing clustered index. A general recommendation is to create CCI with DOP=1 to keep the prefect ordering.
From these two articles, queries which hit a small percentage of columns and stick to a relatively small number of rowgroups will likely perform better. For people who understand normal B-tree indexes, the second point seems clear enough, but the first point is at least as important.
So having read up on automatic soft-NUMA, I was eager to see what it did with my main production servers when I upgraded them. My main pair of production servers (they are paired into an Availability Group) have 4 NUMA nodes with 16 physical cores per node and hyperthreading for a total of 32 logical cores per node with 1.5 TB of RAM. Obviously, we are using core-based Enterprise Edition for these servers. I thought I knew what automatic soft-NUMA would do, and wanted to confirm if my expectations were right.
Read on, but it looks like there’s a “to be continued…” here.
When compiling a SQL query that references an external table stored in an HDFS file, the PDW Engine Service contacts the Hadoop Namenode for information about the file. This information, combined with the number of DMS instances in the PDW cluster, is used to calculate the portion (offset and length) of the input file(s) each DMS instance should read from HDFS. This information is passed to DMS in the HDFS Shuffle step of the DSQL (distributed SQL) plan along with other information needed to read the file, including the file’s path, the location of the appropriate Namenode, and the name of the RecordReader that the bridge should use.
The system attempts to evenly balance the number of bytes read by each DMS instance. Once the DMS instances obtain split information from the Namenode, each can independently read the portion of the file it is assigned, directly communicating with the appropriate Datanodes without any centralized control.
This is a very clear paper which helps describe the core constructs of Polybase. Highly recommended.
An open source approach helps build a foundation for other models attempting to forecast violations at food establishments. The analytic code is written in R, an open source, widely-known programming language for statisticians. There is no need for expensive software licenses to view and run this code.
Read on for more details and check out their GitHub repo.
In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.
- Scatter Plot
- Bar & Stack Bar Chart
- Box Plot
- Area Chart
- Heat Map
We’ll use ‘Big Mart data’ example as shown below to understand how to create visualizations in R. You can download the full dataset from here.
That’s a nice set of visuals, covering a broad swath of potential visualization scenarios.