Re: Need explanation on Parent and Child Storage Group in VMAX All Flash Array

One example, from the Oracle space, is to create a child SG (storage group) for the Oracle data files (e.g. data_sg) and another for the redo logs (e.g. redo_sg). Aggregate them under a parent SG (e.g. database_sg). Then use the parent for operations that relate to the whole database:

1. Masking view.

2. Remote replications with SRDF (create a consistency group that includes both data and logs).

3. Local database snapshots using SnapVX (a snapshot containing both data and log is considered ‘restartable’ and you can use it to instantiate new copies of the database, or as a ‘savepoint’ before patch update or for any other reason).

However, if you need to recover production database, you only want to restore data_sg and not redo_sg (in case the online redo logs survived, as they contain the latest transactions). Therefore, although the snapshots are made with the parent (database_sg), you can restore just the child (data_sg), and proceed with database recovery – all the way to the current redo logs.

Another advantage is separate performance monitoring of the database data files and logs. When using the child SG’s you can monitor them separately for performance KPIs without the redo logs metrics mix up with the data files.

Finally, with the introduction of Service Levels (SL’s) back into the code, you can use them differently on the child SG’s if you so wanted (e.g. Silver for data_sg, gold for redo_sg, etc.)

Related:

2019: The Year of the Data-Driven Digital Ecosystem

By Jeff Clarke, Vice Chairman of Products and Operations for Dell Technologies



It’s that time of year – our planet has made its trip around the sun and as we close out 2018, we look ahead and think about the possibilities for 2019. And we’re closing in on the next decade of innovation that takes us into 2030, where we at Dell Technologies predict we’ll realise the next era of human-machine partnerships – where we will be immersed in smart living, intelligent work, and a frictionless economy.



We made some bold predictions last year – some coming to fruition a bit faster than others…there’s still much to do in advancing artificial intelligence and machine learning technologies, and autonomous systems are continuing to take shape as organisations build the digital backbone to support them.



So what’s in store for 2019? Read on to see our top predictions for 2019 as we enter the data-driven digital ecosystem.



We’ll be more immersed than ever in work and life



Virtual assistants continue to be pervasive in consumer technology – smart home technologies, “things” and connected cars – learning your preferences and proactively serving up content and information based on previous interactions. We’ll see this machine intelligence merge with augmented and virtual reality in the home to create truly immersive experiences – like a virtual sous chef that can help you whip up an easy meal for the family. And you’ll be more connected to your personal health with even more intelligent wellness tracking devices that can capture more information about the body, like heart rate variability (HRV), sleep patterns and more that you can easily share with health care providers for better care.



Immersive intelligence will also follow us to work. Our PCs and devices we use every day will continue to learn from our habits and proactively boot up with the right apps and services at the right time. Advances in natural language processing and voice technologies will create a more productive dialogue with machines, while automation and robotics will create faster, more fluid collaboration with technology to get more done. And, with augmented and virtual reality applications creating on- and off-site immersive experiences – people will have access to the data they need to do work whenever, wherever they are.



Data gold mine will spark next “Gold Rush” in tech investments



Organisations have been stockpiling big data for years. In fact, it’s predicted that by 2020, the data volume will reach 44 Trillion gigabytes or 44 Zettabytes. That’s a lot of data. Soon they’ll finally put it to work as digital transformation takes shape.



As they derive more value from that data – with insights driving new innovations and more efficient business processes – more investments will be born out of the technology sector. New startups will emerge to tackle the bigger challenges that make AI a reality: data management and federated analytics where insights can be driven from virtually everywhere, and data compliance solutions for a safer, smarter way to deliver amazing outcomes.



5G will have us livin’ on the edge



The first 5G devices are slated to hit the market sometime next year with the much-anticipated next-generation network that promises to completely change the data game in terms of speed and accessibility. Low-latency, high-bandwidth networks mean more connected things, cars and systems – and a boat load of AI, Machine Learning and Compute happening at the edge, because that’s where all the data will be generated.



It won’t be long before we begin to see micro-hubs lining our streets – mini datacenters if you will – that will also give rise to new “smart” opportunities for real-time insights happening on the corner of your street.



Cities and towns will become more connected than ever, paving the way for smart cities and digital infrastructure that we predict will be thriving in 2030. And it’ll be a game changer for industries like healthcare or manufacturing, where data and information being generated out in the field can be quickly processed and analysed in real time – versus having to travel back and forth to a cloud – and then readily shared with those who need it.



Data forecast will call for more clouds



Last year we predicted the arrival of the Mega Cloud – a variety of clouds that make up a powerhouse operating model as IT strategies require both public and private clouds. So far that’s holding true. The public vs. private cloud debate will continue to wane as organisations realise that they need to effectively manage all the different types of data they’ll be processing. A recent IDC survey pointed to more than 80% of respondents repatriating data back to on-premise private clouds – and we can expect that trend to continue, even with projections for public cloud growth.



Multi-cloud environments will drive automation, AI and ML processing into high gear because they give organisations the ability to manage, move and process data where and when they need to. In fact, we’ll see more clouds pop up as data becomes increasingly distributed – at the edge in autonomous car environments or in smart factories, in cloud-native apps, in protected on-prem centers to meet a host of new compliance and privacy standards and of course, the public cloud for a variety of apps and services that we use every day.



Move over Millennials, Gen Z will clock into the workplace



Millennials are going to have to make room for the next generation with Gen Z (born after 1995) badging into the workplace over the next year – creating an increasingly diverse workforce spanning five generations! This will create a rich range of experiences in life and technology. 98% of Gen Z will have used technology as part of their formal education, many already understand the basics of software coding and expect only the best technology to be a part of their work experience.



Gen Z will spark a new evolution in technology innovation for the workplace and create more opportunities for technology literacy and on-site learning for new skills with older generations of workers. AR and VR will become increasingly commonplace and close the skills gap across an aging workforce – while giving Gen Z the speed and productivity they demand.



No more weak links or waste: Supply chains will get stronger, smarter and greener



Believing in the many advantages to running a sustainable business, organisations will follow our lead and begin to accelerate ways to design waste out of their business models through new innovation in recycling and closed loop practices. To help, we at Dell are sharing our blueprint for turning ocean bound plastics into recycled packaging and turning soot from diesel generator exhaust fumes into ink for printing on boxes.





We’ll see advances in supply chain traceability, by scrutinising and harnessing emerging technologies to identify precise opportunities to course correct. Blockchain will likely play a role as well, to ensure trust and safety in sourcing, while also securing information and data about goods and services along the way.



There’s never been a better time for technology – with innovation in 5G, AI and Machine Learning, cloud and blockchain throttling full steam ahead. I’m willing to bet that we’ll make great use of those 44 zettabytes of data in 2020. We’ll unlock the power of data in ways never imagined before, transforming everyday business and everyday life. So buckle up – we’re riding full speed into the Data Era – and 2019 is going to be one heck of a year.

Related:

Introduction of IBM DB2 Archive logging leveraged by Avamar DB2 plugin

Article Number: 504655 Article Version: 3 Article Type: How To



Avamar Plug-in for IBM DB2 7.4.101-58,Avamar Plug-in for IBM DB2 7.3.101-125,Avamar Plug-in for IBM DB2 7.5.100-183

The intention of this article is introducing DB2 Archiving logging function, which will be leveraged by Avamar DB2 backup.

Archive logging is used specifically for rollforward recovery. Archived logs are log files that are copied from the active log path to another location. You can use one or both of the logarchmeth1 or logarchmeth2 database configuration parameters to allow you or the database manager to manage the log archiving process.

Active and archived database logs in rollforward recovery. There can be more than one active log in the case of a long-running transaction.

User-added image

Taking online backups is only supported if the database is configured for archive logging. During an online backup operation, all activities against the database are logged. When Avamar online backup is restored, the logs must be rolled forward at least to the point in time at which the backup operation completed. For this to happen, the logs must be archived and made available when the database is restored. After Avamar online backup is complete, the database manager forces the currently active log to be closed, and as a result, it will be archived. This ensures that Avamar online backup has a complete set of archived logs available for recovery.

The logarchmeth1 and logarchmeth2 database configuration parameters allow you to change where archived logs are stored. The logarchmeth2 parameter enables you to archive log files to a second separate location. The newlogpath parameter affects where active logs are stored.

Unless you specify that you want to manage the active logs (by using the LOGRETAIN value), the database manager removes log files from the active log path after these files are archived and they are no longer needed for crash recovery. If you enable infinite logging, additional space is required for more active log files, so the database server renames the log files after it archives them. The database manager retains up to 8 extra log files in the active log path for renaming purposes.

Related:

Re: proxycp.jar error running -selectalldatastore

I am not exactly sure when or why this stopped working but the “selectdatastore” command is no longer working. This is a command I use quite often as we are always adding datastores and moving VMs around. Other commands are still working. I did download the latest version of proxcp, but it did not help. Thanks in advance for any assistance.

Here is what I get…

:/tmp/#: java -jar proxycp.jar -selectalldatastore

======================= Starting ProxyCP ==========================

Proxycp : v2.80

Date : November 14, 2018 8:09:19 AM EST

COMMAND : java -jar proxycp.jar -selectalldatastore

VDP/Nemo Detected : false

——————————————————————————————————————-

Nov 14, 2018 8:09:22 AM com.avamar.mc.security.registry.ProviderRegistryBasic register

INFO: Provider “JsafeJCE version 6.21” is registered at position 10

———————————–BACKUP—————————————–

Backup File generated to Rollback Datastore Map : /data01/avamar/var/log/BackupProxyMap-2018-11-14_09-09-22.sql

For Rollback, run the following command and then restart MCS

psql -p 5555 mcdb -U admin -f /data01/avamar/var/log/BackupProxyMap-2018-11-14_09-09-22.sql

—————————————————————————-

Error : java.lang.NullPointerException

——————————————————————————————————————-

Closing Connection Pool : November 14, 2018 8:09:23 AM EST

Related:

proxycp.jar error running -selectalldatastore

I am not exactly sure when or why this stopped working but the “selectdatastore” command is no longer working. This is a command I use quite often as we are always adding datastores and moving VMs around. Other commands are still working. I did download the latest version of proxcp, but it did not help. Thanks in advance for any assistance.

Here is what I get…

:/tmp/#: java -jar proxycp.jar -selectalldatastore

======================= Starting ProxyCP ==========================

Proxycp : v2.80

Date : November 14, 2018 8:09:19 AM EST

COMMAND : java -jar proxycp.jar -selectalldatastore

VDP/Nemo Detected : false

——————————————————————————————————————-

Nov 14, 2018 8:09:22 AM com.avamar.mc.security.registry.ProviderRegistryBasic register

INFO: Provider “JsafeJCE version 6.21” is registered at position 10

———————————–BACKUP—————————————–

Backup File generated to Rollback Datastore Map : /data01/avamar/var/log/BackupProxyMap-2018-11-14_09-09-22.sql

For Rollback, run the following command and then restart MCS

psql -p 5555 mcdb -U admin -f /data01/avamar/var/log/BackupProxyMap-2018-11-14_09-09-22.sql

—————————————————————————-

Error : java.lang.NullPointerException

——————————————————————————————————————-

Closing Connection Pool : November 14, 2018 8:09:23 AM EST

Related:

Applying a Factory Model to Artificial Intelligence and Machine Learning

EMC logo


We’ve understood for a long time that organizations who spend more on, and are better at, deriving value from their data using analytics significantly outperform their peers in the market. All of us also know, because we feel it, that the pace of change is ever increasing.  I see this all the time with the customers I work with, many of whom seem to be suffering from the “Red Queen” effect – each having to change and innovate faster just to keep standing still, let alone make progress against a tide of change.

I’ve also had cause to re-read Salim Ismail’s book, “Exponential Organizations”, recently which got me thinking: in order to compete, we should be designing and building solutions that allow us to exponentially grow our capacity to create value from data in order to meet business demand. Importantly though, how do we do that without also exponentially growing infrastructure costs (a bad idea) and the number of Data Scientists employed (an impossible dream)? That’s a really great exam question and one I’d like to explore in this blog.

Scaling Analytics with a Factory Model

I think that one of the reasons Artificial Intelligence and Machine Learning (AI / ML) are on the radar of all CxOs these days is because they’re seen as a way of closing the yawning gap most companies have between their capacity to collect data and their ability to apply it in the form of actionable insights. In other words, there’s a certain amount of ‘magical thinking’ going on here and AI/ML is the new magic being applied.

Our view is that the answer to our exam question lies in a more industrialized process.  We have been using a factory model concept with our customers to help them to address this central question of scaling efficiently. Take a look at the model in its entirety and then I’ll dissect it.

Download the Interactive Infographic [best viewed in full screen mode].

# 1: How do you drive innovation with AI/ML technologies?

As AI / ML technologies, packaging, frameworks and tooling are emerging so rapidly, there’s a real need to evaluate these new capabilities with a view to understanding the potential impact they might have on your business. The right place to do that is an R&D Lab.

At this point, we’re just trying to assess the technology and identify the potential business value and market impact. Is it a potentially disruptive technology that we need to start thinking about, perhaps disrupting ourselves before we get disrupted by others?  Or, it may just be a slightly better mousetrap than the one we are already using. By assessing the technology at the edge, we can answer questions around the planning horizon and take the appropriate steps to introduce the technology to the right parts of the business so it can be evaluated more completely.

The most important thing to bear in mind here is that this is a critical business function. It can’t be seen as a purely academic exercise conducted by an isolated team. Disruption is a modern reality and an existential threat to every business – the R&D function is a strategic investment that links you and your business to tomorrow’s world.

Development can happen both ways around of course. As well as technology-led, it might be that your Lean Innovation team is scanning the technology horizon to fill engineering gaps in a product that’s being brought to market. Close cooperation between these teams resulting in a melting pot of innovation is exactly what’s needed to survive and thrive over the long term. Today is the least amount of change you will ever see – we had all better get used to it!

The goal, regardless of whether development started from an idea sparked from the technology or the business side, is to become something of significance to the organization. It could be adding a net new product to the current portfolio, together with the corresponding line in the annual chart of accounts, or perhaps a more fundamental change is needed to maximize its potential, spinning it out to a completely new company.  If you’re interested in reading more around this topic, I’d recommend reading Geoffrey Moore’s book, “Zone to Win”.

As strategic developments progress, they will mature from Horizon 3 to Horizon 2 and finally into the more immediate Horizon 1, assuming they continue to be viewed as adding value to the business. At this point, if you haven’t already done so, you may like to stop here and quickly read my previous blog Industrializing the Data Value Creation Process, that looked at a conceptual framework for thinking about the way we extract commercial value from data – it might help you understand the process side of what I’m about to explain!

#2: How do you prioritize Horizon 1 activities?

At its heart, given the infinite demand and finite resources available in most organizations, you need to decide what you are going to spend your time on – this prioritization challenge needs to be based on a combination of factors, including overall strategy, likely value and current business priorities, as well as the availability of the data required.

Download the Interactive Infographic [best viewed in full screen mode].

The data doesn’t need to be available in its final form at this stage of course, but you may need to have at least some accessible to start the discovery process. Besides, data has a nasty habit of tripping you up, as it almost always takes longer to sort out legal and technical issues than you think, so addressing these kinds of challenges before you begin the data discovery work is normally a sound investment.

If data is the new oil, then the first, and most crucial step is discovering the next reserve under the ground. In our case, we’re typically using AI/ML to find a data pattern that can be applied to create commercial value. That discovery process is really crucial so we need to ensure our Data Scientists have the right environment and tools available so we have the best possible chance of finding that oil if it’s down there!

#3: How do you maximize Data Scientist productivity?

We know from experience that one size really doesn’t fit all, especially when it comes to Data Science. Some problems will be data heavy and others data light. Some will require extensive time spent data wrangling while others use heavy-weight GPU acceleration to plough through deep and computationally heavy neural networks. Libraries and tooling is also very likely to be different and may be driven by the personal preferences of the Data Scientists doing the work. Now, while you could force them all to use the one environment and one set of tools, why would you do that if your goal is to maximize productivity and employee satisfaction? The very last thing you need if you’re trying to scale up the Data Science work you’re doing is for your Data Scientists to be walking out of the door because they don’t like the setup. While I’m all in favor of standardization where it makes sense, technology has really moved past the point where this is strictly necessary.

If you scale Data Science by crowding all of your Data Scientists around a single production line with just the one set of tools and shared resources, they can’t help but get in each other’s way. Besides, the production line will inevitably be running at the pace of the slowest Data Scientist….or worse, the production line may even break because of the experiments one Data Scientist is undertaking.

It’s not that Data Scientists don’t collaborate and work in teams – it’s more that each will be much more productive if you give them a separate isolated environment, tailored specifically to the challenge they are faced with and tools they know. That way they get to independently determine the speed of the production line, which tools they use and how they are laid out. See my related blog Applying Parenting Skills to Big Data: Provide the Right Tools and a Safe Place to Play…and Be Quick About It!.

#4: How do you address data supply chain and quality issues?

Just like at a production line you might see at BMW or Ford, if we want to avoid any interruptions in production we need to ensure our supply chain delivers the right parts just in time for them to be assembled into the end-product. In our case this is all about the data, with the end product being a data product of some kind such as a classification model that could be used to score new data or perhaps just the scored results themselves.

As we never want to stop the production line or fail our final assembly, we also need to make sure the data is of an acceptable quality level. Since we don’t want to do that validation right next to the production line, we need to push the profiling and validation activity as far upstream as we can so it doesn’t interfere with the production line itself and any quality problems can be properly addressed.

#5: How do you scale compute and storage?

With a suitable infrastructure in place, combined with access to the right data and tooling, the Data Scientist is all set to do their work.

In most, if not all cases, the Data Scientist will need read access to governed data that is in scope for the analysis, along with the ability to upload external data assets that could be of value. They will also need to be able to iteratively save this data as the source datasets are integrated, wrangled and additional data facets generated to improve model performance. In traditional environments, this might mean a significant delay and additional costs as data is replicated multiple times for each Data Scientist and each use case, but it doesn’t have to happen that way! The other advantage with moving away from a legacy Direct Attach Storage (DAS) approach is that most Network Attached Storage (NAS) and cloud deployments provide change on write snapshot technologies, so replicas take near zero additional capacity and time to create in the first place with only the changed data consuming any capacity.

While we’re on the topic of cost and scale, of course the other thing you want to independently scale is the compute side of things. As I’ve already mentioned, some workloads will be naturally storage heavy and others compute heavy and storage light.  Data Science discovery projects also tend to be ephemeral in nature, but that’s also true of many production workloads such as ETL jobs. By leveraging the flexibility of virtualized infrastructure and dynamically managing resources, you can scale them up and down to match performance needs.  In this way, you can manage the natural variations in business activity and complimentary workloads to dramatically increase server utilization rates.  That heavy ETL load you process at the end of each financial period could be scaled out massively overnight when the Data Science team isn’t using the resources and scaled back when they are.  Through a virtualized approach, we can create differently shaped environments and make better use of the resources at our disposal. A simple control plane makes operational considerations a non-issue.

Once the discovery task is completed, the Data Scientist will want to save their work for future reference and share any new artefacts with their peers. Assuming they found something of value that needs to be put into production, they can prepare a work package that can be dropped into the Agile Development team’s engineering backlog.

#6: How do you accelerate the time to production?

The Agile Development team will typically include a blend of Data Architects, Agile Developers and junior Data Scientists with work prioritized and picked off the backlog based on available resources, as well as effort estimates and business priorities.

The same rules apply to the Agile Development team as they did for the Data Scientists.  Keeping them busy and effective means making sure they have everything they need at their disposal. Waiting for suitable development and analytical environments to be provisioned or data to be authorized or secured is not a good use of anyone’s time!  Using the same virtualized approach, we can quickly create an environment for the agile team that includes a more limited set of Data Science tooling (for scoring models) and the tool chain needed for the development work.

All provisioned in seconds, not weeks or months.

The next stage in the route to production for our data product will be more formal User Acceptance Testing (UAT). We can use our virtualized as-a-Service provisioning yet again here, only this time, rather than including the Agile Developers’ tool chain, we’ll include the testing infrastructure in the environment build instead.

The other aspect of efficiency worth noting is that for the most part the time allocated to the Data Science, development and testing tasks is very predictable. The Data Scientists’ work will often be time boxed – producing the best model possible within a set amount of time. In a traditional approach, additional delays are introduced because nobody could predict when the Data Science work would actually be started because of the unpredictable nature of provisioning delays. Addressing this one issue means that each team has a much better chance of sticking to schedule, making the entire process more dependable.

Once development and testing is completed, we need to move our new data product into a production setting. As discussed previously, some workloads are ephemeral in nature while others are not, often because they are stateful or their workloads can’t be resumed if they were to fail for some reason. Operationalizing the workload means selecting the appropriate environment based on its characteristics and then implementing and instrumenting it up appropriately.  This is an interesting topic in its own right and worthy of a follow-up blog!

#7 How do you know if the model is performing as designed?

Having changed the business process in some fashion because of our new data product, we need to have some way of monitoring its performance – ensuring our real-world results are as expected, triggering either management attention or a simple model rebuild when performance declines below acceptable limits.

In practice, this can often mean adding a new measure or report to existing BI solution or real-time monitoring dashboards. To facilitate this, the Agile Development team may have already created an additional SQL view describing performance that can be simply picked up and consumed by the BI team, greatly simplifying implementation.

Putting It All Together

To achieve success with Artificial Intelligence and Machine Learning, it’s critical that you have the right teams within your organization, including Data Scientists, R&D, Lean Innovation, and Agile Development, as well as an industrialized ‘data factory’ process that enables you to get value from your data as efficiently as possible. Technology of course plays a critical role as well, as you need to be able to provision environments quickly and securely, provide tool flexibility, and have the optimal infrastructure in place to support Data Science workloads.

At Dell EMC, we work with customers at all stages of analytics maturity to plan, implement and optimize solutions and infrastructure that enable organizations to drive value from data and support advanced techniques, including artificial intelligence and machine learning. That includes working across the people, process and technology aspects in a grounded and pragmatic fashion to accelerate the time to value and maximize Big Data investments.

If you’re looking for a trusted partner to help you on your analytics journey and take advantage of the latest technologies and techniques, Dell EMC Consulting is here to help. Learn more about our services and solutions and contact your account rep to discuss further. I also welcome you to join the conversation by posting your thoughts or questions below.

Before you go

Make sure to download the Factory Model for Artificial Intelligence and Machine Learning Interactive Infographic [best viewed in full screen mode].

 

The post Applying a Factory Model to Artificial Intelligence and Machine Learning appeared first on InFocus Blog | Dell EMC Services.


Update your feed preferences


   

   


   


   

submit to reddit
   

Related:

XD 7.X XACT_ABORT support

When SET XACT_ABORT is ON, if a Transact-SQL statement raises a run-time error, the entire transaction is terminated and rolled back.

When this option is enabled on a SQL instance hosting a XenDesktop 7.X database, unexpected might occur at brokering time, or when attempting to reconnect to an active session.

Related:

Re: Warning: RecoverPoint copy contains volumes from more than one VPLEX CG

Dear all,

I have this warning in my RP configuration:

RecoverPoint copy contains volumes from more than one VPLEX consistency group. It is recommended a RecoverPoint copy contain volumes from only one VPLEX consitency group. VPLEX consistency group: [No VPLEX CG,produzione] ; VX-DCSIT-RPA



I checked all the CG,and I did not find volumes replicated in more than one CG.

Can someone help me understand this warning?

Regards,

Jacopo

Related:

Warning: RecoverPoint copy contains volumes from more than one VPLEX CG

Dear all,

I have this warning in my RP configuration:

RecoverPoint copy contains volumes from more than one VPLEX consistency group. It is recommended a RecoverPoint copy contain volumes from only one VPLEX consitency group. VPLEX consistency group: [No VPLEX CG,produzione] ; VX-DCSIT-RPA



I checked all the CG,and I did not find volumes replicated in more than one CG.

Can someone help me understand this warning?

Regards,

Jacopo

Related:

Provisioning Services: PVS Servers May Stop Responding Or Target Devices May Freeze During Startup Due To Large Size Of MS SQL Transaction Logs

Backup the XenApp/XenDesktop Site and PVS database and the Transaction log file to trigger the Transaction log auto truncation.

The transaction log should be backed up on the regular basis to avoid the auto growth operation and filling up a transaction log file.

Reference: https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/back-up-a-transaction-log-sql-server?view=sql-server-2017​

ADDITIONAL INFORMATION

Ideally Transaction log will be truncated automatically after the following events:

  • Under the simple recovery model, unless some factor is delaying log truncation, an automatic checkpoint truncates the unused section of the transaction log.In the Simple recovery there is little chance for the transaction log growing – just in specific situations when there is a long running transaction or transaction that creates many changes
  • By contrast, under the full and bulk-logged recovery models, once a log backup chain has been established, automatic checkpoints do not cause log truncation. Under the full recovery model or bulk-logged recovery model, if a checkpoint has occurred since the previous backup, truncation occurs after a log backup (unless it is a copy-only log backup). There is no automated process of transaction log truncation, the transaction log backups must be made regularly to mark unused space available for overwriting. Bulk-logged recovery model reduces transaction log space usage by using minimal logging for most bulk operations

Transaction log file size may not decrease even if transaction log has been truncated automatically.

Log truncation frees space in the log file for reuse by the transaction log. Log truncation is essential to keep the log from filling. Log truncation deletes inactive virtual log files from the logical transaction log of a SQL Server database, freeing space in the logical log for reuse by the Physical transaction log. If a transaction log were never truncated, it would eventually fill all the disk space that is allocated to its physical log files.

It is recommended also to keep the transaction log file in a separate drive from the database data files, as placing both data and log files on the same drive can result poor database performance.

Related: