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Day: May 8, 2020

Technology Choices for Streaming Pipelines

The Hadoop in Real World team takes us through different tools available when working on streaming pipelines:

Businesses want to get insights as quickly as possible and do not want to wait for a day, like before, to bring up a report to understand what happened till yesterday. They require a more proactive approach that can help to act immediately when something significant happens and also to prevent the system from any faults/downtime before it occurs. Imagine you are buying some product from an e-retailer and you have gone till the point to make payment and something happened that caused the payment not to go through successfully. At that very moment, you are having a second thought about whether to buy the product now or later. Suppose, if the business is getting a report of this occurrence next day, it would not be of much use for them as the customer would have already bought it from somewhere or decided against it. This is where real-time events and insights come in. If it were a real-time report, the team would have called up the customer and made the purchase by offering some discounts, which in turn would have changed the mind of the customer.

Click through for a high-level discussion of these tools.

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Project Metamorphosis: Elastic Kafka Clusters

Jay Kreps explains what Confluent has been up to lately:

What is Project Metamorphosis?

Let me try to explain. I think there are two big shifts happening in the world of data right now, and Project Metamorphosis is an attempt to bring those two things together.

The first one, and the one that Confluent is known for, is the move to event streaming.

Event streams are a real revolution in how we think about and use data, and we think they are going to be at the core of one of the most important data platforms in a modern company. Our goal at Confluent is to build the infrastructure that makes that possible and help the world take advantage of it. That’s why we exist.

But event streaming isn’t the only paradigm shift we’re in the midst of. The other change comes from the movement to the cloud.

Click through for the high-level. I can see this even more directly competing with Kinesis and Event Hubs.

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Polygon-Based Spatial Searches with Cosmos DB

Hasan Savran continues a series on spatial data in Cosmos DB:

I want to continue to develop our new map application for Azure Cosmos DB. So far, we can run a custom spatial query in Cosmos DB and display the results on a map. I want my users to create a polygon on map and search for data under this polygon. If you are familiar with Zillow, that is how their application lets you look for houses to buy or rent. You select an area, and Zillow application displays all available houses or rental under the area you draw. It is extremely useful and user-friendly search.

Click through to see how Hasan does it, as well as getting around a coordinate ordering problem.

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Using Computed Columns to Avoid Scans without Changing Queries

Andy Mallon shares a trick you don’t want to use too often, but can get you out of a pinch:

We’ve all been there. You’ve got a query where the JOIN or WHERE predicate is not SARGable. You’ve read about how this can be a problem, and how bad it is for performance.

Alas, you cannot change the query. Sometimes this reason is political, sometimes it’s because you’ve got a third-party app and simply don’t have access to the code. But you do have access to the database…

This is the type of thing you learn about and use maybe twice in your career, and then you get frustrated with the third-party vendor which won’t fix their code.

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A History of Bad Habits

Aaron Bertrand has a compendium of bad habits, anti-patterns, and Festivus-quality grievances to air:

Going back more than a decade, I’ve been writing and presenting about what I call “bad habits” – typically shortcuts or sub-optimal ways to do things in SQL Server. Often users just don’t realize these things are bad or that there is a better way.

Here is an ongoing list of articles that I consider to be along these lines – eradicating bad habits or at least promoting best practices. Not all are explicitly framed as a “bad habit,” but they do all present things I wish I observed less often.

Click through for a disturbingly long list of items.

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mssql-cli Generally Available on MacOS and Linux

Alan Yu announces the general availability of mssql-cli outside of Windows:

We are excited to announce that mssql-cli is now generally available on macOS and Linux.

mssql-cli is an open source and cross-platform command-line tool (CLI) to manage SQL Server on-prem and on the cloud. We are a proud member of the dbcli family of open source command line tools to manage relational databases.

If you are a user of sqlcmd, you will love the interactive and modern design components in mssql-cli. With this release, you will also be able to use mssql-cli in non-interactive scenarios such as scripting and automation. Read on to learn more about how mssql-cli will help improve your productivity through a modern CLI experience.

If you love the command line (or simply need to SSH into a box from time to time), give this product a try.

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Removing Redundant Indexes

Guy Glantser helps find and remove redundant indexes in SQL Server:

For some reason, which I have never understood, SQL Server allows you to create duplicate indexes on the same object (table or view). You can create as many non-clustered indexes as you like with the exact index keys and included columns as well as the exact index properties. The only difference between the indexes would be the index ID and the index name. This is a very undesirable situation, because there is clearly no benefit from having the same index more than once, but on the other hand there is quite a lot of overhead that each index incurs. The overhead includes the storage consumed by the index, the on-going maintenance during DML statements, the periodical rebuild and/or reorganize operations, and the additional complexity that the optimizer has to deal with when evaluating possible access methods to the relevant table or view.

I don’t fully agree with Guy’s definition of redundancy, but it’s more a quibble than anything else—if I have an index on (A,B,C) and an index on (A,B), it might seem redundant, but there are cases when it isn’t. For example, suppose C is a large NVARCHAR column such that we barely fit (A,B,C) into the window for an index (1700 bytes in SQL Server 2016, 900 in prior versions), but A and B are INT types. If we have a lot of cases where we need (A,B) but not C, that second index is definitely not redundant.

Regardless, click through for Guy’s argument and a script to help you find potentially redundant indexes.

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Columnstore and Memory-Optimized tempdb

Erik Darling has a bucket of cold water for us:

In SQL Server 2019:

– Exciting stuff: In memory tempdb!
– Exciting stuff: sp_estimate_data_compression_savings can evaluate columnstore compression!
– Disappointing stuff: If you use in memory tempdb, you can’t have any columnstore anything in tempdb

That means if you’re using sneaky tricks like clustered columnstore indexes on temp tables to induce batch mode, you’re gonna get a lot of errors.

Likewise, you won’t be able to evaluate if columnstore will help your tables.

Click through to understand the extent of this limitation. Hopefully this is something we see addressed in vNext and a CU for 2019.

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