Articles

External Article

How to determine SQL Server database transaction log usage

One crucial aspect of all databases is the transaction log. The transaction log is used to write all transactions prior to committing the data to the data file. In some circumstances the transaction logs can get quite large and not knowing what is in the transaction log or how much space is being used can become a problem. So how to you determine how much of the transaction log is being used and what portions are being used?

2017-10-24

4,163 reads

External Article

Overview of Azure Data Lake

Azure Data Lake stores petabytes of data and analyzes trillions of objects in one place with no constraints. Data Lake Store can store any type of data including massive data like high-resolution video, medical data, and data from a wide variety of industries. Data Lake Store scales throughput to support any size of analytic workload with low latency. Read on to learn more.

2017-10-20

3,483 reads

External Article

Simple SQL: Random Thoughts

How does one get a truly random sample of data of a certain size from a SQL Server database table. Well, there are simple non-portable tricks one can use, such as the NewID() function, but then refining those can be tricky. Take the Rand() function for a start. Can it really provide you with a truly random number? Why doesn't the TABLESAMPLE clause give you a set number of rows? Joe Celko scratches his head a bit, explains some of the issues and invites some suggestions and tricks from readers.

2017-10-19

3,623 reads

External Article

The Power of Python and SQL Server 2017

Python is new to SQL Server 2017. It is intended primarily to allow the use of Python-based machine-learning within SQL Server, but it can be used for far more than this, with any Python libraries or Frameworks. To provide an example of what is possible, Hitendra shows how to use the feature securely to provide intelligent application caching, where SQL Server can automatically indicate when data changes to trigger a cache refresh.

2017-10-16

5,024 reads

Blogs

Stop Using Pandas for Aggregations — Try DuckDB Instead

By

If you've ever loaded a 2 GB CSV into pandas just to run a...

Understanding Fabric Ontology

By

What problem is Fabric Ontology trying to solve? For years, most data conversations have...

QUOTENAME Basics: #SQLNewBlogger

By

Recently I ran across some code that used a lot of QUOTENAME() calls. A...

Read the latest Blogs

Forums

The New Software Team

By Steve Jones - SSC Editor

Comments posted to this topic are about the item The New Software Team

Database Mail in SQL Server 2022

By Abdellateef Ibrahim

Comments posted to this topic are about the item Database Mail in SQL Server...

The string_agg function

By Alessandro Mortola

Comments posted to this topic are about the item The string_agg function

Visit the forum

Question of the Day

The string_agg function

We create the following table and then insert some records in it:

create table t1 (
   id int primary key,
   category char(1) not null,
   product varchar(50)
);

insert into t1 values
(1, 'A', 'Product 1'),
(2, 'A', 'Product 2'),
(3, 'A', 'Product 3'),
(4, 'B', 'Product 4'),
(5, 'B', 'Product 5');
What happens if we execute the following query in both Sql Server and PostgreSQL?
select id, 
category, 
string_agg(product, ';')
                 over (partition by category order by id
                 rows between unbounded preceding and unbounded following) as stragg
from t1;

See possible answers