The SQL LENGTH function returns the number of characters in a string. The LENGTH function is available in many Database Management Systems (DBMS).
The LENGTH Function Syntax
LENGTH Function Notes
If the input string is empty, the LENGTH returns 0.
If the input string is NULL, the LENGTH returns NULL.
Length Function Across Databases
When working as a technical consultant, one has to work with customer’s databases and as you move from one database to another you will find that the function commands may vary–assuming the database has an equivalent function.
Working with VQL and SQL Server got me thing about the LENGTH() function, so, here is a quick references list, which does include the SQL Server.
Data analytics has changed where data is no longer manageable in relational databases only. Data is flowing from various sources which are not of the same format. This means it is not possible to store all data in the same repository. Some are best suited for storing in relational databases, others for Apache Hadoop while others are best suited for NoSQL databases.
During data analyzing, so much time is taken in trying to bring the distributed data together instead of obtaining insights. Db2 Federation has come to the rescue of data analysts. Federation concept in db2 eliminates the need for storing data in different repositories and reduces the hassle of getting insights.
What is DB2 Federation?
DB2 federation is a data integration technology that permits remote database objects to be accessed as local DB2 database objects. This technology connects multiple databases and makes them appear like one database.
How does DB2 federation work?
Federation allows you to access all of your data that is on multiple distributed databases using a single query. When implemented in an organization, this technology can be used to access data that is on any of the organization’s Db2, whether local or in the cloud.
Why use DB2 federation?
So, why should you use the federation? This concept brings data of all formats into one virtual source. With data being retrieved from one virtual source, analyzing it becomes cost-effective and efficient.
What are its primary use cases for DB2 federation?
Merging of various sources of data
DB2 federation facilitates consolidating of data from sources data local and cloud to form one virtual data source. This eliminates the process of migrating data which can be expensive and troublesome.
Increase the capacity of a repository beyond the fixed limits
Physical storage capacity is bound to have a limit which is one reason you may find an organization has distributed its data in various repositories. With federation, the storage is virtual and therefore doesn’t have any limit. This technology can greatly help you if your physical dataset is running low on space.
Linking up to Db2 Warehouse on Cloud
People who use Db2 products can federate data from Db2 on Cloud and Db2 Warehouse on the Cloud. This will give them a joint interface where they can access, add, query, and analyze data without encountering the complex ETL processes. Better still, no additional code will be required to execute all these processes. This makes it easy for people with the low technical know-how to use these products smoothly.
Split data across different servers
At times, you might choose to partition your data. With federation integration technology, partitioned data can be queried with a unified interface. Federation allows you to better balance your workloads, scale precise parts of an app, and create micro-services that work harmoniously.
Generally, db2 federation makes it access data by bringing it together into a single virtual source. This brings about cost and time-saving benefits. When you want to analyze data, you can get insights immediately instead of spending a lot of time querying through repositories.
I’ve tried to explain the difference between OLTP systems and a Data Warehouse to my managers many times, as I’ve worked at a hospital as a Data Warehouse Manager/data analyst for many years. Why was the list that came from the operational applications different than the one that came from the Data Warehouse? Why couldn’t I just get a list of patients that were laying in the hospital right now from the Data Warehouse? So I explained, and explained again, and explained to another manager, and another. You get the picture. In this article I will explain this very same thing to you. So you know how to explain this to your manager. Or, if you are a manager, you might understand what your data analyst can and cannot give you.
OLTP stands for On Line Transactional Processing. With other words: getting your data directly from the operational systems to make reports. An operational system is a system that is used for the day to day processes. For example: When a patient checks in, his or her information gets entered into a Patient Information System. The doctor put scheduled tests, a diagnoses and a treatment plan in there as well. Doctors, nurses and other people working with patients use this system on a daily basis to enter and get detailed information on their patients. The way the data is stored within operational systems is so the data can be used efficiently by the people working directly on the product, or with the patient in this case.
A Data Warehouse is a big database that fills itself with data from operational systems. It is used solely for reporting and analytical purposes. No one uses this data for day to day operations. The beauty of a Data Warehouse is, among others, that you can combine the data from the different operational systems. You can actually combine the number of patients in a department with the number of nurses for example. You can see how far a doctor is behind schedule and find the cause of that by looking at the patients. Does he run late with elderly patients? Is there a particular diagnoses that takes more time? Or does he just oversleep a lot? You can use this information to look at the past, see trends, so you can plan for the future.
The difference between OLTP and Data Warehousing
This is how a Data Warehouse works:
The data gets entered into the operational systems. Then the ETL processes Extract this data from these systems, Transforms the data so it will fit neatly into the Data Warehouse, and then Loads it into the Data Warehouse. After that reports are formed with a reporting tool, from the data that lies in the Data Warehouse.
This is how OLTP works:
Reports are directly made from the data inside the database of the operational systems. Some operational systems come with their own reporting tool, but you can always use a standalone reporting tool to make reports form the operational databases.
Pro’s and Con’s
There is no strain on the operational systems during business hours
As you can schedule the ETL processes to run during the hours the least amount of people are using the operational system, you won’t disturb the operational processes. And when you need to run a large query, the operational systems won’t be affected, as you are working directly on the Data Warehouse database.
Data from different systems can be combined
It is possible to combine finance and productivity data for example. As the ETL process transforms the data so it can be combined.
Data is optimized for making queries and reports
You use different data in reports than you use on a day to day base. A Data Warehouse is built for this. For instance: most Data Warehouses have a separate date table where the weekday, day, month and year is saved. You can make a query to derive the weekday from a date, but that takes processing time. By using a separate table like this you’ll save time and decrease the strain on the database.
Data is saved longer than in the source systems
The source systems need to have their old records deleted when they are no longer used in the day to day operations. So they get deleted to gain performance.
You always look at the past
A Data Warehouse is updated once a night, or even just once a week. That means that you never have the latest data. Staying with the hospital example: you never knew how many patients are in the hospital are right now. Or what surgeon didn’t show up on time this morning.
You don’t have all the data
A Data Warehouse is built for discovering trends, showing the big picture. The little details, the ones not used in trends, get discarded during the ETL process.
Data isn’t the same as the data in the source systems
Because the data is older than those of the source systems it will always be a little different. But also because of the Transformation step in the ETL process, data will be a little different. It doesn’t mean one or the other is wrong. It’s just a different way of looking at the data. For example: the Data Warehouse at the hospital excluded all transactions that were marked as cancelled. If you try to get the same reports from both systems, and don’t exclude the cancelled transactions in the source system, you’ll get different results.
online transactional processing (OLTP)
You get real time data
If someone is entering a new record now, you’ll see it right away in your report. No delays.
You’ve got all the details
You have access to all the details that the employees have entered into the system. No grouping, no skipping records, just all the raw data that’s available.
You are putting strain on an application during business hours.
When you are making a large query, you can take processing space that would otherwise be available to the people that need to work with this system for their day to day operations. And if you make an error, by for instance forgetting to put a date filter on your query, you could even bring the system down so no one can use it anymore.
You can’t compare the data with data from other sources.
Even when the systems are similar. Like an HR system and a payroll system that use each other to work. Data is always going to be different because it is granulated on a different level, or not all data is relevant for both systems.
You don’t have access to old data
To keep the applications at peak performance, old data, that’s irrelevant to day to day operations is deleted.
Data is optimized to suit day to day operations
And not for report making. This means you’ll have to get creative with your queries to get the data you need.
So what method should you use?
That all depends on what you need at that moment. If you need detailed information about things that are happening now, you should use OLTP. If you are looking for trends, or insights on a higher level, you should use a Data Warehouse.
Here is a table quick reference of some common database and/or connection types, which use connection level isolation and the equivalent isolation levels. This quick reference may prove useful as a job aid reference, when working with and making decisions about isolation level usage.
Occasionally, a client will want a list of tools to work with Netezza / PureData, other than the Netezza Administrator Client. Honestly, there are several tools which could be used, if they have odbc and/or jdbc connectivity. However, these are the tools which keep being used across different customers.
For customers willing to work with an open-source tool Aginity for Netezza provides a significant set of capabilities, including script generation, which can be a significant productivity accelerator for development and operations support teams.
This is one of the items, which we need to do in Netezza SQL from time to time, but is not exactly obvious to the average SQL user. So, having a pattern for making the conversion for an epoch field to a timestamp can be a real time saver.
I cannot count the times, which using a flag (also, called an indicator) is described as a nice to have in database table design, at least, until the code runs into complexity and/or performance challenges.
When designing your data models, ETL’s, and reports it is useful to consider how indicator flags can help. While indicator flags are, normally, binary in nature, such as True/False or Yes/No, but indicator flags don’t always need to be binary.
How indicator flags can help your processes and reporting:
Provide an equijoin for complex business rules, which can otherwise result ‘Not In List’, ‘In list’, ‘Not Exists., ‘Exists’, ‘Not Equal To’ and sub-selects SQL statements
Provide processing maker to prevent look ups to other tables to determine an attribute. For example, a snapshot type (daily, weekly, monthly) flag, which can be used to apply data retention rules.
Provide a mark for a special circumstance. For example, a Legal Hold flag to mark record to be exempted from removal and/or change to meet legal proceeding requirements.
The judicious planning and use of flags can reduce the number of full table scans required against large tables.
Here are a few pointers for building an IBM InfoSphere Information Server (IIS) isjdbc.config file for an IBM DB2 Universal Driver, Type 4.
Where to place JAR files
For Infosphere Information Server installs, as a standard practice, create a custom jdbc file in the install path. And install any download Jar file not already installed by other applications in the jdbc folder. Usually, jdbc folder path looks something like this:
This is an extract table I created from the IBM source, a while back when investing what format to convert data fields into for IBM Infosphere Datastage. I have had it floating around in my notes, but lately, I have found myself referencing it to help other team members, so, it seems useful to include it here. The notes column is just a few snippets of information, which I have found useful to reference when planning data field conversions.
Transformer data type
DB2 data type
Netezza data type
NUMERIC or DECIMAL
DOUBLE PRECISION or FLOAT(15)
The maximum character string size is 64,000.
REAL or FLOAT(6)
DB2 9.1 TIME data type does not support fractional digits or microseconds.
DB2 9.1 TIME data type does not support fractional digits or microseconds.
The maximum character string size is 64,000.
InfoSphere Information Server InfoSphere Information Server 11.5.0 – Datatypes
Recently, I reason to know the Coordinated Universal Time (UTC) Time zone offset of the Netezza database for incorporation into an ETL. So, here is an easy Aginity Workbench SQL to pull Time Zone information from your database, if you have permission to the _VT_PG_TIME_OFFSET view.
Effective practices are enablers, which can improve performance, data availability, environment stability, resource consumption, and data accuracy.
Use of an Enterprise Scheduler
The scheduling service in InfoSphere Information Server (IIS) leverages the operating system (OS) scheduler, the common enterprise scheduler can provide these capabilities beyond those of a common OS scheduler:
Centralized control, monitoring, and maintenance of job stream processes
Improved insight into and control of cycle processes
Improved intervention capabilities, including alerts, job stream suspension, auto-restarts, and upstream/downstream dependency monitoring
Reduced time-to-recovery and increased flexibility in recovery options
Improved ability to monitor and alert for a mission-critical process that may be delayed or failing
Improved ability to automate disparate process requirements within and across systems
Improved load balancing to optimize the use of resources or to compensate for the loss of a given resource
Improved scalability and adaptability to infrastructure or application environment changes
Use of data Source Timestamps
When they exist or can be added to data, ‘created’ and ‘last updated’ timestamps can greatly reduce the impact of Change Data Capture (CDC) operations. Especially, if the data warehouse, data model and load process store that last successful run time of CDC jobs. This reduces the number of rows required to be processed and reduces the load on the RDBMS and/or ETL application server. Leveraging ‘created’ and ‘last updated’ can, also, greatly reduce the processing time required to perform the same CDC processes.
Event-based scheduling, when coupled with an Enterprise scheduler, can increase data availability, distribute work opportunistically. Event-based scheduling can allow all or part of a process stream to begin as soon as predecessor data sources have completed the requisite processes. This can allow processes to begin as soon as possible, which can reduce resource bottlenecks and contention. This, potentially, allows data to be made available earlier than a static time-based schedule. Event-based scheduling can also delay processing, should the source system requisite processing completion be delayed; thereby, improving data accuracy in the receiving system.
Integrated RDBMS Maintenance
Integrating RDBMS Maintenance into the process job stream can perform on-demand optimization as the processes move through their flow, improving performance. Items such as indexing, distribution, and grooming, maintenance at key points ensure that the data structures are optimized for follow on processes to consume.
Application Server and Storage Space Monitoring and Maintenance
Monitoring and actively clearing disk space can not only improve overall performance, and reduce costs, but it also improves application stability.
Data Retention Strategies
Data Retention strategies, an often overlooked form of data maintenance, which deals with establishing policies ensure only truly necessary data is kept and that information by essential category, which is no longer necessary is purged to limit legal liability, limit data growth, storage costs, and improve RDBMS performance.
Use Standard Practices
Use of standard practices both, application and industry, allows experienced resources to more readily understand the major application activities, their relationships, dependency, design, and code. This facilitates resourcing and support over the life cycle of the application.
Tuning SQL is one of those skills, which is part art and part science. However, there are a few fundamental approaches, which can help ensure optimal SQL select statement performance.
Structuring your SQL
By Structuring SQL Statements, much performance can be gained through good SQL statement organization and sound logic.
Where Clause Concepts:
Use criteria ordering and Set Theory thinking. SQL can be coupled with set-theory to aid conception of the operations being conducted. Order your selection criteria to execute criteria which arrives at the smallest possible row set first. Doing so reduces the volume of rows to be processed by follow-on operations. This does require an understanding of the data relationships to be effective.
Join Rules (equijoins, etc.)
When constructing your joins, consider these rules:
Join on keys and indexed columns: The efficiency of your program improves when tables are joined based on indexed columns, rather than on non-indexed ones.
Use equijoins (=), whenever possible
Avoid using of sub-queries
Re-write EXISTS and NOT EXISTS subqueries as outer joins
Avoid OUTER Joins on fields containing nulls
Avoid RIGHT OUTER JOINS: Always select FROM your primary table (or derived table) and LEFT OUTER JOIN to auxiliary tables.
Use Joins Instead of Subqueries: A join can be more efficient than a correlated subquery or a subquery using IN. Use caution when specifying ORDER BY with a Join: When the results of a join must be sorted, limiting the ORDER BY to columns of a single table can cause the database to avoid a sort.
Provide Adequate Search Criteria: When possible, provide additional search criteria in the WHERE clause for every table in a join. These criteria are in addition to the join criteria, which are mandatory to avoid Cartesian products
Order of Operations SQL & “PEMDAS”
To improve your SQL, careful attention needs to be paid to the mathematical order of operations; especially, parentheses since they not only set the order of operation but also the boundaries of each subset operation.
PEMAS is “Parentheses, Exponents, Multiplication and Division, and Addition and Subtraction”.
Use parentheses () to group and specify the order of execution. SQL observes the normal rules of arithmetic operator precedence.
If the parentheses are nested, the expression in the innermost pair is evaluated first. If there are several un-nested parentheses, then parentheses are evaluated left to right.
* / %
Multiplication Division Modulus
If there are several, evaluation is left to right.
If there are several, evaluation is left to right.
Index Leveraging (criteria ordering, hints, append, etc.)
Avoid Full Table Scans: within the scope of a SQL statement, there are many conditions that will cause the SQL optimizer to invoke a full-table scan.
with NULL Conditions (Is NUll, Is Not NUll)
Against Unindexed Columns
with Like Conditions
with Not Equals Condition (<>, !=, not in)
with use built-in Function (to_char, substr, decode, UPPER)
Use UNION ALL instead of UNION if business rules allow
UNION: Specifies that multiple result sets are to be combined and returned as a single result set. Query optimizer performs extra work to return to avoid duplicate rows.
UNION ALL: Incorporates all rows into the results. This includes duplicates. Query optimizer just needs to concatenate the result sets with no extra work
Use stored procedures instead of ad hoc queries when possible. Stored procedures are precompiled and cached
Avoid cursor use when possible
Select only the rows needed
Use NOLOCK hint in the select statement to avoid blocking
Commit transactions in smaller batches
Whenever possible use tables instead of views
Make sure comparison columns whether using JOIN or WHERE clause are exactly the same data type. For example, if we are comparing Varchar column to nchar columns the query optimizer has to do a CONVERT before comparing the values
Note: You do not necessarily need to remove all full table scans from your query’s execution plan. Tables with few rows, few columns, or thin columns may fit into few database blocks. In this case, a full table scan will always be the most efficient access
If we consider Materialized Views (MV) in their simplest form, as a point in time stored query result, then materialized views serve two primary purposes Performance Optimization and Semantics Simplification.
There are several ways in which materialized views can improve performance:
Reduce Database Workloads: materialized views can reduce database workloads by pre-assembling frequently used queries and, thereby, eliminating the repetitive execution of joins, aggregations, and filtering.
FacilitateDatabase Optimizers: in some databases can be partitioning and indexing which are considered by database optimizers. Also, some databases, in which more than one materialized view has been applied to a table, the database optimizer will consider all the associated materialized views when optimizing queries.
Reduced Network Workloads: by the use of database replication and/or mass deployment techniques to materialized views, they can be distributed to more local proximity to the consumers, thereby, reducing the data volume across the network and provided business continuation/disaster recovery capabilities, should the primary site become temporarily unavailable.
Precalculation and/or Preaggregation: Performing calculation and aggregation of information upon creation of a materialized view, eliminates the need to perform these functions on an on-demand basis as various consumers submit requests.
Data Subsets: by applying filters to eliminate unnecessary data (e.g. history data no longer in common reporting use) or unnecessary data attributes (e.g. unused columns or columns intended for other information purposes) the impact of filter for these items is reduced and is effectively eliminated for consumers of the materialized view.
Materialized views can be used to simplify the semantics provided to information consumers with Ad hoc capabilities and/or to simplify the construction of reporting and analytics objects. Depending on the database and/or integration tools in use to create them, materialized views can simplify the consumer experience by:
Reduce or Eliminate Join Coding: When constructed materialized views can perform the joins and populate the materialized view with the value results of from the join table, thereby, eliminating the need for the consumer to perform this function as an ad hoc user or in the semantics of reporting and analytics tools
Pre-application of Business Rules: When constructed materialized views can apply business rules to facilitate queries by adding indicator flags and pre-applying special business logic to data and populating the materialized view with the value results, thus, eliminating the need for the consumer to perform this function as an ad hoc user or in the semantics of reporting and analytics tools.
Precalculation and/or Pre-aggregation: Performing calculation and aggregation of information upon creation of a materialized view allow the consumer to use the results without the need to build the calculations and/or aggregations into the ad hoc query or in the semantics of reporting and analytics tools. This also helps to ensure information accuracy and consistency.
Data Subsets: By prefiltering the data during the creation of the view unnecessary or unused data and columns are not available to consumers and do not need to filter out of ad hoc queries or in the semantics of reporting and analytics tools