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Inner join is the most common type of Join which is used to combine the rows from two tables and create a result set containing only such records that are present in both the tables based on the joining condition (predicate). Below is an example of INNER JOIN and the resulting set. Because of this if we perform inner join, then Department 5 does not appear in the above result. However in the below query we perform an outer join (dept left outer join emp), and we can see this department. The above SQL can be alternatively written as below (will yield the same result as above). SELECT dept. name DEPARTMENT, emp. EMPLOYEE. FROM DEPT dept LEFT OUTER JOIN EMPLOYEE emp. ON dept. id = emp. For example, if we have the department ID of each employee, then we can use this department ID of the employee table to join with the department ID of department table to lookup department names. Union does not require any condition for joining. For example, if you have 2 employee tables with same structure, you can UNION them to create one result set that will contain all the employees from both of the tables. The difference is, WHERE clause can only be applied on a static non- aggregated column whereas we will need to use HAVING for aggregated columns. Suppose we want to see only those departments where department ID is greater than 3. There is no aggregation operation and the condition needs to be applied on a static field. We will use WHERE clause here. SELECT * FROM DEPT WHERE ID > 3. Next, suppose we want to see only those Departments where Average salary is greater than 8. Here the condition is associated with a non- static aggregated information which is “average of salary”. We will need to use HAVING clause here. SELECT dept. name DEPARTMENT, avg(emp. AVG. We will use two different queries to extract data from our emp table and then we will perform UNION, MINUS and INTERSECT operations on these two sets of data. This is an example of how a hierarchy (in this case employee- manager hierarchy) is stored in the RDBMS table. Now, suppose if we need to print out the names of the manager of each employee right beside the employee, we can use self join. See the example below. SELECT e. name EMPLOYEE, m. MANAGER. FROM EMPLOYEE e, EMPLOYEE m. WHERE e. mgr. If we perform inner join, this employee will not show- up. In fact, the method I am going to show below is not very generic either. This method only works if there is at least one unique column in the table. This method will also work if there is no single unique column, but collection of columns that is unique. Anyway, here is the query. SELECT name, sal, (SELECT COUNT(*) FROM EMPLOYEE i WHERE o. Here sort key is “name” column. For this technique to work, the sort key needs to be unique. We have chosen the column “name” because this column happened to be unique in our Employee table. If it was not unique but some other collection of columns was, then we could have used those columns as our sort key (by concatenating those columns to form a single sort key). We have done an explicit sorting on the row. But notice that name column is also sorted (which is probably the reason why this column is referred as sort- key). If you want to change the order of the sorting from ascending to descending, you will need to change “> =” sign to “< =” in the query. This is why many databases already implement other methods to achieve this. For example, in Oracle database, every SQL result set contains a hidden column called ROWNUM. We can just explicitly select ROWNUM to get sequence numbers. The problem here is how do you define which record is first and which is second. Which record is retrieved first from the database is not deterministic. It depends on many uncontrollable factors such as how database works at that moment of execution etc. So the question should really be – “how to select any 5 records from the table?” But whatever it is, here is the solution. WHERE ROWNUM < = 5. SELECT TOP 5 * FROM EMP. Generic solution. I believe a generic solution can be devised for this problem if and only if there exists at least one distinct column in the table. For example, in our EMP table ID is distinct. We can use that distinct column in the below way to come up with a generic solution of this question that does not require database specific functions such as ROWNUM, TOP etc. I could very well take ID column as well. If yes, post your solution in the comment. So ORDER BY ROWNUM does not work. If two records tie for second place, no record will be assigned the 3rd rank as no one came in third, according to RANK. See below. SELECT name, sal, rank() over(order by sal desc) rank. 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Data Compression comes in two flavors: Row Compression Page Compression Row Compression Row compression changes the format of physical storage of data. It minimizes the metadata (column information, length, offsets, etc.) associated with each record. Numeric data types and fixed length strings are stored in variable- length storage format, just like Varchar. It uses the following techniques to compress data: Row compression Prefix Compression For every column in a page, duplicate prefixes are identified. These prefixes are saved in compression information headers (CI) which reside after page headers. A reference number is assigned to these prefixes and that reference number is replaced wherever those prefixes are being used. Dictionary Compression. Dictionary compression searches for duplicate values throughout the page and stores them in CI. The main difference between prefix and dictionary compression is that prefix is only restricted to one column while dictionary is applicable to the complete page. Reference. Use Ctrl+Left/Right to switch messages, Ctrl+Up/Down to switch threads, Ctrl+Shift+Left/Right to switch pages. VMware Storage v. Motion, Data Movers, Thin Provisioning, Barriers to Monster VM’s. I’ve recently been testing some Monster VM’s in my lab and setting up templates to provision these Monster VM’s more rapidly. Along the way I’ve run into a few things I wasn’t aware of that could be a potential barrier to Monster VM’s in some ways, and some interesting behaviour during large storage migrations. When you start provisioning Monster VM’s more often and move them around you are quite likely to run into the same problems. So hopefully this article will help you work around the problems I hit and avoid them until there is a more permanent solution. How to Efficiently Deploy Virtual Machines from. Both of those can be changed later after you deploy from this template. Michael White wrote a good article on Building a Windows 2012 R2 VMware Template. I also hope that our friends at VMware are aware of this behaviour and work towards addressing it in an upcoming release so that Apps can continue to love v. Sphere as much as they do today, even when the scale increases and they become the norm. I would like to thank Duncan Epping, Andrew Mitchell and Frank Denneman. When I say Monster VM in this case I mean a VM with 8. TB of disk (4 v. SCSI Controllers and 1.
VMDK’s), 1. 6 v. CPU’s and 1. GB RAM. So it’s pretty big, but not massive compared to the maximums that v. Sphere 5. x can support. But this was big enough for the experiments I was going to run with Microsoft Exchange 2. Exchange 2. 01. 3 and Oracle 1. R2. The Problem and Why it’s a Barrier to Monster VM’s. So what’s the problem? Well the problem is I was creating templates for these Monster VM’s so I can provision them rapidly after my experiments. These templates are thin provisioned so I don’t waste unnecessary space. Also the systems I deployed from these templates and was running the experiments on were also thin provisioned. Where the problem comes in is I had put these VM’s on my bulk NAS storage until I need them. Deploy an OVA or OVF Template on VMware ESXi seancavcisco. Deployment of a Virtual Machine on a UCS Server Using OVA Template for UCCX - Duration. This was so they don’t take up space on my higher more valuable tiers of storage (See my lab environment). I hear you saying, so what could possibly cause a problem with that? Well that is the interesting part. It all comes down to a thing called a Data Mover. A Data Mover is the component within VMware v. Sphere that moves data from place to place (funny that) when doing operations such as Storage v. Motion and other types of storage migrations (clones). In v. Sphere today (4. Data Movers. The three Data Movers are FSDM, which is the most simple, most compatible and slow data mover (highest level in the IO stack too), FS3. DM, more advanced and faster, and FS3. DM Hardware Accelerated (offloads to the array). I will not go into all the details here as I think the best way to understand these is to read Duncan and Frank’s Book – v. Sphere 5. 1 Clustering Deep Dive, and Frank’s article. My i. SCSI Array (HP Stor. Virtual) and one of my NFS systems (Nutanix NX- 3. VAAI enabled, my other NAS (QNAP 8. Pro, QNAP 4. 69) don’t have VAAI yet. This is where I hit a little snag. Going between arrays regardless of their VAAI capability, won’t use FS3. DM Hardware Accelerated Data Mover, that’s only used within the same array. But going between a NAS and VMFS5, or from one NAS to another NAS won’t even use FS3. DM. It will only use FSDM, the most compatible, but also the slowest and simplest of the Data Mover species. Why is this so important I hear you say? Let me explain. FSDM will read every block of a VM, even if it has never been written to. You read that correctly, even if the block has never existed, it will be read and transferred. This is very bad news if you have a Monster VM or Monster VM Template using Thin Provisioning. This effects every vendor using NFS with VMware v. Sphere. One of the reasons it probably isn’t that well known or noticeable is that most VM’s have very small storage footprints, relatively speaking, especially when they are initial provisioned. Large production VM’s also don’t move around all that much. So in my case one of the VM’s had 8. TB allocated, 5. 6. TB in use, another VM had 5. TB allocated and only 1. GB in use. It copied over all the real data, and then for all the VMDK’s that had zero bytes physically written on disk it proceeded to copy TB’s worth of zeros for every block that had never yet been written to. The storage migration took 3 days to complete! In case you were wondering I have a 1. G network but the QNAP NAS is dual 1. G connected. In contrast to this, when I migrated or cloned the template VM with 1. GB used and 5. TB allocated from VMFS5 to VMFS5 and it used the FS3. DM data mover (I did this across arrays) it took all of 2. VMFS5- VMFS5 across arrays) vs 3 days (NAS- VMFS5, NAS- NAS). You see my problem. The Workaround or Temporary Solutions. So I have considered a number of solutions or workarounds. I could mount an i. SCSI LUN on my NAS and use VMFS5 to store my templates and make sure their destination is also VMFS5. This really isn’t ideal, as there are a lot of advantages to NFS, simplicity being one of them. Still wouldn’t help if I needed to deploy the template on an NFS datastore. Only put the OS disks on my templates and leave off all the other VMDK’s and SCSI controllers until after the VM’s are provisioned. This isn’t ideal either as I’ll have to re- provision all the apps and data each time I want to re- create a VM and run a test from a known good state. I could provision and set up a VM on each of the storage devices I’m going to use for my experiments. This option is also not ideal, as I’ll have to build these templates on each storage device. But at least being thin provisioned they won’t take up much space initially, at least for the templates. Instead of using Storage v. Motion I could use Backup and Restore, with say VMware VDP and v. ADP (v. Sphere API for Data Protection). This is potentially a good option, and might be quite fast. I could set up v. Cloud Director and use Fast Provisioning. Provided I set up the template and use the same underlying datastore for all the copies this would work fine. This is one of the advantages of NFS, you get one huge volume, but VM level granularity. If I wanted to provision a copy to another datastore though, it would probably be hit by the same problem. I could just copy the files using the filesystem and not using a Storage v. Motion or cloning. This is fine in my lab environment where the VM’s will likely be powered off when I want to move them about. But in a production environment where the VM’s need to remain powered on, this is not going to work. In a production environment if I was looking to migrate a Monster VM that was running a database or Exchange or similar I would probably set up a new instance on the destination and use the application level utilities to migrate the data and switch over. Your requirements will vary, so evaluate the best options for your situation. Putting this in perspective. In order for you to come experience this you need to be migrating cloning or deploying VM templates that have a source or destination on different storage devices, backed by an NFS Datastore, have Thin Provisioned Storage, and have a large storage allocation 1. TB+. So right now this might not impact many. But the numbers will grow over time. Especially when you consider that many Cloud environments, especially those using v. Cloud Director, are backed by NFS storage, as it’s more efficient for Cloud. Although many environments may still be using block storage right now for the majority of their workloads, I think in the future that will change. Many systems are now running on NFS, and many v. Sphere environments are now backed by NFS Datastores. This will increase in the future. Final Word. I think the real solution is that the process VMware uses for migrating large VM’s between arrays where a NAS or NFS is involved, especially those that might be thin provisioned, needs to change. The FSDM Data Mover doesn’t cut it, not for Monster VM’s. Copying TB’s worth of zeros that have never been written to is of absolutely no value. It can be done much smarter. Perhaps this might come in the form of some new VAAI for NAS Primitive. Perhaps VMware just invents a new Data Mover. They have many smart people and are committed to Monster VM’s and running these types of workloads in a Software Defined Datacenter or Hybrid Cloud, so I’m sure in due course a viable long term solution will become available. Not to be reproduced for commercial purposes without written permission. The PDFin plug-in allows you to open and edit PDF drawings within AutoCAD. Trial with watermark. Auto. CAD Electrical - Free download and software reviews. Please describe the problem you have with this software. 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