Table variables use tempdb similar to how temporary tables use tempdb. Table variables are not in-memory constructs but can become them if you use memory optimized user defined table types. Often I find temporary tables to be a much better choice than table variables. The main reason for this is because table variables do not have statistics and, depending upon SQL Server version and settings, the row estimates work out to be 1 row or 100 rows. In both cases these are guesses and become detrimental pieces of misinformation in your query optimization process.
It’s worth the read.
The tutorial covers many different techniques for training predictive models at scale, and deploying the trained models as predictive engines within production environments. Among the technologies you’ll use are Microsoft R Server running on Spark, the SparkR package, the sparklyr package and H20 (via the rsparkling package). It also touches on some non-Spark methods, like the bigmemory and ff packages for R (and various other packages that make use of them), and using the foreach package for coarse-grained parallel computations. You’ll also learn how to create prediction engines from these trained models using the mrsdeploy package.
Check out the post as well as the tutorial David links.
Now, you’ve defined your source and we can start creating processors that’ll do the work on the data. The first goal is to mask the credit card numbers recorded in the incoming purchase records. The first processor is used to convert credit card numbers from 1234-5678-9123-2233 to xxxx-xxxx-xxxx-2233. The Stream.mapValues method performs the masking. The KStream.mapValues method returns a new KStream instance that changes the values, as specified by the given ValueMapper, as records flow through the stream. This particular KStream instance is the parent processor for any other processors you define. Our new parent processor provides the masked credit card numbers to any downstream processors with Purchase objects.
Unfortunately, this article seems like a mixture of high-level and low-level information that appeals more to people who already know how Kafka Streams works, but it is nevertheless interesting.
In this post you build encryption and decryption into sample Kinesis producer and consumer applications using the Amazon Kinesis Producer Library (KPL), the Amazon Kinesis Consumer Library (KCL), AWS KMS, and the aws-encryption-sdk. The methods and the techniques used in this post to encrypt and decrypt Kinesis records can be easily replicated into your architecture. Some constraints:
AWS charges for the use of KMS API requests for encryption and decryption, for more information see AWS KMS Pricing.
You cannot use Amazon Kinesis Analytics to query Amazon Kinesis Streams with records encrypted by clients in this sample application.
If your application requires low latency processing, note that there will be a slight hit in latency.
Check it out, especially if you’re thinking about streaming sensitive data.
This is an example of tiered system design. Tiered system is a system design pattern where data is categorized and stored in different data stores that match best to each category. It can both improve performance and lower the cost of a system. One of the most famous tiered system designs is computer memory hierarchy. In the log analytics use case, analysts mostly search for logs in recent months, but often run batch jobs to get long term trends from logs in recent years. Therefore, recent logs are indexed and stored in Solr for search, while years of logs are stored in HBase for batch processing. As such, the index in Solr is small, which both improves performance and reduces cost, among other benefits.
Although only months of logs are stored in Solr, the logs before that period are stored in HBase and can be indexed on demand for further analysis.
Now that we have covered a high level architecture of a log analytics system, we will dive into more details of individual components.
This looks like a solid architecture for a logging system and can apply to other cases as well.
With respect to page layout, reports have two sizing modes: interactive and printable. When users run a report in their web browser and use it interactively, they typically don’t care that much about the page size. This is particularly true with reports that have wide content like a matrix region that can dynamically grow horizontally with the data. When a report is printed or rendered to a print- able format like a PDF or Word file, we need to be mindful about fitting the content on pages.
The report designer does not make page sizing and dimensions particularly obvious so it’s an easy thing to miss. Fortunately, the science behind page sizing is pretty simple. Page dimension properties are grouped into two objects that you can select in the designer; these are shown in Figure 7-1. With the Properties window visible, click outside the report body to show properties for the report. Here you will see the InteractiveSize and PageSize properties. Expand these to see the individual Width and Height properties for each group.
Read on to get the better part of a full chapter’s worth of material.
Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. Amazon Kinesis is ideal for Internet of Things (IoT) use cases. It can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, Raspberry Pi gadgets, devices, social media, operational logs, metering data and more.
With Amazon Kinesis, you can build real-time dashboards, capture exceptions, execute algorithms, and generate alerts. With point-and-click menus, you can ingest data, query it and then send output to a variety of destinations including but not limited to Amazon S3, Amazon EMR, Amazon DynamoDB, or Amazon Redshift.
Kinesis is powerful, especially if you’re already locked into the AWS platform. My preference is Apache Kafka, but Kinesis is definitely worth learning about.
My first thought was that perhaps there is some process that runs against the production system and the test system that goes to sleep with an open transaction, holding an X or an IX lock against this table. If the index create can’t get its shared lock, then it could be part of a blocking chain.
So I asked first if the index create was the head of the blocking chain, or if it was perhaps blocked by something else. The answer came back that no, the index create was NOT blocked. It was holding the shared lock for a long time.
My new friend even sent a screenshot of the index create running against the test instance in sp_WhoIsActive with blocking_session_id null.
Read on for the full story and keep those systems patched.
Even though they are estimated to cost the same (50% for each one) with the estimated cost of 0.275286 to be more precise in this sense.
To be more precise in the reality you will notice the Aggregate Predicate Pushdown taking place on the first query, while the second query is using the Storage Engine to read out all of the 2 million rows from the table and filter it in the Hash Match iterator.
Actual Number of Locally Aggregated Rows is the one property on the Columnstore Index Scan iterator that will give you an insight on what happened within the Columnstore Index Scan, since the Aggregate Predicate Pushdown is not shown as a filter on the property. This is not the most fortunate solution as far as I am concerned, but since the 0 rows flowing out of the Columnstore Index Scan will serve as a good indication that Aggregate Predicate Pushdown took place, but if you want to be sure of all the details you will need to check the properties of the involved iterators.
Definitely worth reading.
Boring old disclaimer: What I am describing here is undocumented, unsupported, likely to change between versions, and will probably make you go blind. In fact, the depth of detail exposed illustrates one reason why Microsoft would not want to document it: if end users of SQL Server found a way to start relying on this not changing, it would hamstring ongoing SQL Server improvement and refactoring.
With that out of the way, let’s dive right into DBCC TEC, a command which can dump a significant chunk of the object tree supporting a SQL Server session. This result is the same thing that shows up within a dump file, namely the output of the CSession::Dump() function – it’s just that you can invoke this through DBCC without taking a dump (cue staring match with Kendra). Until corrected, I shall imagine that TEC stands for Thread Execution Context.
I appreciate Ewald’s ability to make sense out of the madness of database internals.