As we read a lot of different types of licenses couldn't understand actual daily limit of license that my company have and do it have difference if of number of daily operation based on license type?
This is license that currently have - can we agree that daily operation is same docs here
It's related in my case especially for Power Automate - because when the system reach daily limit automatically scale to very slow close to zero execution.
Related
Is there any offline analytical processing? If there is, what is the difference with online analytical processing?
In What are OLTP and OLAP. What is the difference between them?, OLAP is deals with Historical Data or Archival Data. OLAP is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations.
I don't understand what does the word online in online analytical processing mean?
Is it related to real time processing(About real-time understanding: In a short time after the data is generated, this data can be analyzed. Am I wrong with this understanding)?
When does the analysis happen?
I imagine a design like this:
log generated in many apps -> kafka -> (relational DB) -> flink as ETL -> HBase, and the analysis will happen after data is inserted into HBase. Is this correct?
If yes, why is it called online?
If no, when does analysis happen? Please correct me if this design is usually not in the industry.
P.S. Assuming that the log generated by the apps in a day has a PB level
TLDR: as far as I can tell "Online" appears to stem from characteristics of a scenario where handling transactions with satellite devices (ATM's) was a new thing.
Long version
To understand what "online" in OLTP means, you have to go back to when ATM's first came out in the 1990s.
If you're a bank in the 1990s, you've got two types of system: your central banking system (i.e. mainframe), and these new fangled ATM's connected to it... online.
So if you're a bank, and someone wants to get money out, you have to do a balance check; and if cash is withdrawn you need to do a debit. That last action - or transaction - is key, because you don't want to miss that - you want to update your central record back in the bank's central systems. So that's the transactional processing (TP) part.
The OL part just refers to the remote / satellite / connected devices that participate in the transaction processing.
OLTP is all about making sure that happens reliably.
I have a generic question about Snowflake Cost Estimation.
I have a task which is schedule to execute every 5 min
CREATE TASK mytask1
WAREHOUSE = mywh
SCHEDULE = '5 minute'
WHEN
SYSTEM$STREAM_HAS_DATA('MYSTREAM')
AS
INSERT INTO ... ;
In case if there is no new data in mystream, In that case the task will be skipped.
However will it be costing any money as the task is still runinng.
Please suggest
The only possible cost is a Cloud Services cost for the metadata check to see if the stream has data. Based on how Cloud Services credits are discounted, there is a really good chance that this will never be something you'll see a charge for. You can read up on the cloud services billing here:
https://docs.snowflake.com/en/user-guide/credits.html#cloud-services-credit-usage
CREATE TASK:
SYSTEM$STREAM_HAS_DATA
Validating the conditions of the WHEN expression does not require a virtual warehouse. The validation is instead processed in the cloud services layer. A nominal charge accrues each time a task evaluates its WHEN condition and does not run. The charges accumulate each time the task is triggered until it runs. At that time, the charge is converted to Snowflake credits and added to the compute resource usage for the task run.
Generally the compute time to validate the condition is insignificant compared to task execution time. As a best practice, align scheduled and actual task runs as closely as possible. Avoid task schedules that are wildly out of synch with actual task runs. For example, if data is inserted into a table with a stream roughly every 24 hours, do not schedule a task that checks for stream data every minute. The charge to validate the WHEN expression with each run is generally insignificant, but the charges are cumulative.
Note that daily consumption of cloud services that falls below the 10% quota of the daily usage of the compute resources accumulates no cloud services charges.
Part1 #
As per the pricing policy of snowflakes ,we will be paying based on the usage and we will not be charged if we won't use resources..This is clear.However I Am trying to understand ,is there any chance for reducing the cost if we drop the unused or rarely used warehouses? users and roles that are not been used any more ?I was looking some cost savings in terms of reducing the cloud services cost.
Part 2#
which is the most cost effective way .
1)Allocating separate warehouse for each team who uses the warehouse at specific times
(or)
2)Allocating single warehouse for all them and monitor warehouse load closely,such that if we notice queued load on warehouse then opt scale out option(multi cluster)(S+S)?
Please suggest the best way so that we can reduce overall cost.
there are only two things major things you are charged for disk and cpu, and a couple of minor things like compile time, and inter region IO charges. But users, warehouses, & roles are just access control lists in the end, that are to control cpu and disk usage.
prior to per second billing we found using one warehouse for a couple of teams meant less wasted CPU billing, and to some degree that almost is the case with the min 60 second billing, but we have a shared x-small most teams do dev on, and then spin-up bigger warehouses to run one-off loads (and then shut down) or have auto-scaling clusters to handle "normal load" which we also use cron jobs to limit "max size" just so in the off-peek times we intentionally increase latency of total load, to shift expenditure budget to peek times. and compared to the always running clusters, our dev instances are single digit percentages, so 1 or 2 warehouses is a round error.
The way we found the most value for reducing cost, was to look at the bill and see what seemed more $$ then we expected for the bang we where getting, and then we experimented, to see if there were lower cost ways to reach the same end goal. Be it different shaped tables that we multi inserted into, or finding queries that had long execution times, or pruned lots of rows (which might lead to the first point).. if you are want to save dollars you have to whach/care how you are spending them, and make trade-offs.
Part #1
Existence of multiple Warehouse will not incur any cost, cost will only come when it will be utilized as part of compute. However dropping unused objects will certainly ease the operational effort. Also if user exists and not being used it should fall under your security audit and it is always better to disable a user instead of dropping. Validate all downstream application ETL jobs/BI reports (If any) before dropping any users/roles
Cloud service cost is entirely different ball game , it follows 10% rule. One need to pay this amount when cloud service usage exceeds 10% of the warehouse usage on that day.
Part #2
Snowflake always suggest warehouse should be created based on your activity. Please do not create warehouse to segregate teams/user group. Create user and roles for that.
What we observed
During development keeping only one virtual Warehouse, until real requirement pops up (Project team wise segregation for cost sharing or budgeting or credit assessment) there is no need to have multiple warehouse created.
Even for Prod activity wise segregation is ideal, one for ETL load/BI reporting / Data analytics team
Thanks
Palash Chatterjee
I'm building a system which I plan to deploy on Google App Engine. Current pricing is described here:
Google App Engine - Pricing and Features
I need an estimate of cost per client managed by the webapp. The cost won't be very accurate until I have completed the development. GAE uses such fine grained price calculation such as READs and WRITEs that it becomes a very daunting task to estimate operation cost per user.
I have an agile dev. process which leaves me even more clueless in determining my cost. I've been exploiting my users stories to create a cost baseline per user story. Then I roughly estimate how will the user execute each story workflow to finally compute a simplistic estimation.
As I see it, computing estimates for Datastore API is overly complex for a startup project. The other costs are a bit easier to grasp. Unfortunately, I need to give an approximate cost to my manager!
Has anyone undergone such a task? Any pointers would be great, regarding tools, examples, or any other related information.
Thank you.
Yes, it is possible to do cost estimate analysis for app engine applications. Based on my experience, the three major areas of cost that I encountered while doing my analysis are the instance hour cost, the datastore read/write cost, and the datastore stored data cost.
YMMV based on the type of app that you are developing, of course. If it is an intense OLTP application that handle simple-but-frequent CRUD to your data records, most of the cost would be on the datastore read/write operations, so I would suggest to start your estimate on this resource.
For datastore read/write, the cost for writing is generally much more expensive than the cost for reading the data. This is because write cost take into account not only the cost to write the entity, but also to write all the indexes associated with the entity. I would suggest you to read an article by Google about the life of a datastore write, especially the part about Apply Phase, to understand how to calculate the number of write per entity based on your data model.
To do an estimate of instance hours that you would need, the simplest approach (but not always feasible) would be to deploy a simple app to test how long would a particular request took. If this approach is undesirable, you might also base your estimate on the Google App Engine System Status page (e.g. what would be the latency for a datastore write for a particularly sized entity) to get a (very) rough picture on how long would it take to process your request.
The third major area of cost, in my opinion, is the datastore stored data cost. This would vary based on your data model, of course, but any estimate you made need to also take into account the storage that would be taken by the entity indexes. Taking a quick glance on the datastore statistic page, I think the indexes could increase the storage size between 40% to 400%, depending on how many index you have for the particular entity.
Remember that most costs are an estimation of real costs. The definite source of truth is here: https://cloud.google.com/pricing/.
A good tool to estimate your cost for Appengine is this awesome Chrome Extension: "App Engine Offline Statistics Estimator".
You can also check out the AppStats package (to infer costs from within the app via API).
Recap:
Official Appengine Pricing
AppStats for Python
AppStats for Java
Online Estimator (OSE) Chrome Extension
You can use the pricing calculator
https://cloud.google.com/products/calculator/
We're being asked to spec out production database hardware for an ASP.NET web application that hasn't been built yet.
The specs we need to determine are:
Database CPU
Database I/O
Database RAM
Here are the metrics I'm currently looking at:
Estimated number of future hits to
website - based on current IIS logs.
Estimated worst-case peak loads to
website.
Estimated number of DB queries per
page, on average.
Number of servers in web farm that
will be hitting database.
Cache polling traffic from database
(using SqlCacheDependency).
Estimated data cache misses.
Estimated number of daily database transactions.
Maximum acceptable page render time.
Any other metrics we should be taking into account?
Also, once we have all those metrics in place, how do they translate into hardware requirements?
What I have been doing lately for server planning is using some free tools that HP provides, which are collectively referred to as the "server sizers". These are great tools because they figure out the optimal type of RAID to use, and the correct number of disk spindles to handle the load (very important when planning for a good DB server) and memory processor etc. I've provided the link below I hope this helps.
http://h71019.www7.hp.com/ActiveAnswers/cache/70729-0-0-225-121.html?jumpid=reg_R1002_USEN
What I am missing is a measure for the needed / required / defined level of reliability.
While you could probably spec out a big honking machine to handle all the load, depending on your reliabiltiy requirements, you might rather want to invest in smaller, but multiple machines, and into safer disk subsystems (RAID 5).
Marc
In my opinion, estimating hardware for an application that hasn't been built and designed yet is more of a political issue than a scientific issue. By the time you finish the project, current hardware capability and their price, functional requirements, expected number of concurrent users, external systems and all other things will change and this change is beyond your control.
However this question comes up very often since you need to put numbers in a proposal or provide a report to your manager. If it is a proposal, what you are trying to accomplish is to come up with a spec that can support the proposed sofware system. The only trick is to propose a system that will not increase your cost for competiteveness while not puting yourself at the risk of a low performance system.
If you can characterize your current workload in terms of hits to pages, then you can then:
1) calculate the typical type of query that will be done for each page
2) using the above 2 pieces of information, estimate the workload on the database server
You also need to determine your performance requirements - what is the max and average response time you want for your website?
Given the workload, and performance requirements, you can then calculate capacity. The best way to make this estimate is to use some existing hardware, run a simulated database workload on a database on that hardware, and then extrapolate your hardware requirements based on your data from the first steps.