Image by James Shearer

Its a RDBMs, no its a ColumnStore, no its a cloud integrated storage platform… Oh its actually SQL Server 2014!

Exciting news as the new (project code-name: Hekaton) SQL Server 2014 is community technology preview (CTP1) is out and can be downloaded and evaluated by the community.

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Hashing can be a very useful technique when dealing with the storage and look up of large text fields (say a table of URLs or Search Keywords), these fields will incur high resource utilization on any database engine if used directly in DML statements, in which they are either filtered by or aggregated on. Any index built on these fields is costly to maintain, if it is at all possible given that SQL Server limits index size to 900 bytes.

Using hashing functions we can facilitate the handling of large textual data in the relational engine, leading to improved performance when these fields are being compared to satisfy a query, hashing can also be used to build unique and non-unique indexes that are easier to manage than directly using the text fields in the index definition. In this post we will discuss a few options for hashing large text data using functions native within SQL Server, as well as provide other external  hashing algorithms that we can integrate into Microsoft’s SQL Server (or any RDBMS for that matter) that might provide a better practical performance. 

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Recently I have been involved in researching and building a low-latency high-data-volume OLAP environment for a social entity and interaction analysis platform, the perfect mixture of concepts such as Big Data collection and processing,  large-scale Network Analysis, Natural Language Processing (NLP) and a highly scaled-out OLAP environment for end users to explore and discover data (essentially a Self-Service and Exploratory BI layer).

It is by all means not an easy mission to orchestrate all the technologies that back those concepts, particularly if you are interested in using the optimum solution for the problem at hand, for example Big Data might be better handled by a Hadoop layer, but Hadoop or Hive (at least on their own) are not geared up to respond to OLAP queries, which are real-time by nature, and even if they were, your end-user needs familiar tools and interfaces to analyse and study this data, which is where SQL Server Analysis Service and the whole Microsoft BI stack might come in and offer great integration with already existing business applications (such as Office or SharePoint).

This post discusses a few architectural approaches to exposing a Hadoop layer through a SQL Server Analysis Service (SSAS) interface, with references to data-latency, redundancy and over-all performance.

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Being able to holistically view your SQL Server Job Agent job schedules, how long each job took to run, what other jobs were running at the same time and the status of each job, should be a fundamental part of Job Agent management.

In this post I will be briefly introducing the current SSRS reports integrated into SSMS (or SQL Data Tools) that displays SQL Server Job Agent job information, as well as provide a new report (pictures above) that should give you a better holistic view of activities on your SQL Server Agent, as well as highlight any conflict in job schedules or times were no activity is occurring.

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