I have a disk image. I'm able to see partition start and end values with gparted or another tools. However, I want to calculate them manually. I inserted an image , which showing my disk image partition start and end values. Also, I inserted $MFT file with link. As you see in the picture, my starter point for partition table 2 is : 7968240. How can I determine this number with real calculation ? I tried to dived this value with sector size which is 512. However, results are not fit. I'll appriciate for a formula for it. Start and End Points of Partitions.
$MFT File : https://file.io/r7sy2A7itdur
How can I determine this number with real calculation ?
The information about how a hard disk has been partitioned is stored in its first sector (that is, the first sector of the first track on the first disk surface). The first sector is the master boot record (MBR) of the disk; this is the sector that the BIOS reads in and starts when the machine is first booted.
For the current partition system (gpt) you can get more information here. The MFT is only a part on the NTFS in question, which is calculated via GPT or MBR.
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I have an application which calls the database multiple times to achieve one simple goal.
A little information about this application; In short, the application scrapes data from a webpage & stores specific information from this page into a database. The important information in this query is: Player name, Position. There can be multiple sitting at one specific position, kill points & Class
Player name has every potential to change or remain the same every day
Regarding the Position, there can be multiple sitting in one position
Kill points has the potential to increase or remain the same every day
Class, there is only 2 possibilities that a name can be, Ex: A can change to B or remain A (same in reverse), but cannot be C,D,E,F
The player name can change at any particular day, Position can also change dependent on the kill point increase from the last update which spins back around to the goal. This is to search the database day by day, from the current date to as far back as 2021-02-22 starting at the most recent entry for a player name and back track to the previous day to check if that player name is still the same or has changed.
What is being used as a main reference to the change is the kill points. As the days go on, this number will either be the exact same or increase, it can never decrease.
So now onto the implementation of this application.
The first query which runs finds the most recent entry for the player name
SELECT TOP(1) * FROM [changes] WHERE [CharacterName]=#charname AND [Territory]=#territory AND [Archived]=0 ORDER BY [Recorded] DESC
Then continue to check the previous days entries with the following query:
SELECT TOP(1) * FROM [changes] WHERE [Territory]=#territory AND [CharacterName]=#charname AND [Recorded]=#searchdate AND ([Class] LIKE '%{Class}%' OR [Class] LIKE '%{GetOpposite(Class)}%' AND [Archived]=0 )
If no results are found, will then proceed to find an alternative name with the following query:
SELECT TOP(5) * FROM [changes] WHERE [Kills] <= #kills AND [Recorded]='{Data.Recorded.AddDays(-1):yyyy-MM-dd}' AND [Territory]=#territory AND [Mode]=#mode AND ([Class] LIKE #original OR [Class] LIKE #opposite) AND [Archived]=0 ORDER BY [Kills] DESC
The aim of the query above is to get the top 5 entries that are the closest possible matches & Then cross references with the day ahead
SELECT COUNT(*) FROM [changes] WHERE [CharacterName]=#CharacterName AND [Territory]=#Territory AND [Recorded]=#SearchedDate AND [Archived]=0
So with checking the day ahead, if the character name is not found in the day ahead, then this is considered to be the old player name for this specific character, else after searching all 5 of the results and they are all found to be present in the day aheads searches, then this name is considered to be new to the table.
Now with the date this application started to run up to today's date which is over 400 individual queries on the database to achieve one goal.
It is also worth a noting that this table grows by 14,400 - 14,500 Rows each and every day.
The overall question to this specific? Is it possible to bring all these queries into less calls onto the database, reduce queries & improve performance?
What you can do to improve performance will be based on what parts of the application stack you can manipulate. Things to try:
Store Less Data - Database content retrieval speed is largely based on how well the database is ordered/normalized and just how much data needs to be searched for each query. Managing a cache of prior scraped pages and only storing data when there's been a change between the current scrape and the last one would guarantee less redundant requests to the db.
Separate specific classes of data - Separating data into dedicated tables would allow you to query a specific table for a specific character, etc... effectively removing one where clause.
Reduce time between queries - Less incoming concurrent requests means less resource contention and faster response times to prior requests.
Use another data structure - The only reason you're using top() is because you need data ordered in some specific way (most-recent, etc...). If you just used a code data structure that keeps the data ordered and still easily-query-able you could then perhaps offload some sql requests to this structure instead of the db.
The suggestions above are not exhaustive, but what you do to improve performance is largely a function of what in the application stack you have the ability to modify.
I have an incremental model A where each day is calculated using the previous day's value. Running a full-refresh means that this table needs to be calculated since the beginning of time which is very inefficient and takes too long.
I have tried to create a backup table which will take a copy of the table's value each month, and have model A refer to the backup table during a full-refresh so that the values only after the backup need to be recalculated and I can arrive at today's value much quicker. However this gives me an error:
Encountered an error:
Found a cycle: model.model_A --> model.backup --> model.model_A
This is because the backup refers to the model to get the value each month, while model A also refers to the backup to build off in the case of a full-refresh.
Is there a way around this problem, avoiding rebuilding the entire model from the beginning of time every time I do a full-refresh?
Yes, you can't have 'circular loops' or cycles in your build process.
If there is an application that calculates the values for each day, you could perhaps store the new values back in the same source table(s), just adding a 'updated_at' or something similar. If I understand your use case correctly, you could then use this value whenever you need to query only the last day's information.
Can anbody explain this min/max/mean in the following screenshot
It shows the Minimal, Maximal and Mean number of output files per some predetermined amount of time (my guess a minute in your case).
"
Min/Max/Mean: The minimum, maximum, and mean (arithmetic mean, or average) values are shown. These values are based only on the range of time selected, if any time range is selected. If this instance of NiFi is clustered, these values are shown for the cluster as a whole, as well as each individual node. In a clustered environment, each node is shown in a different color. This also serves as the graph's legend, showing the color of each node that is shown in the graph. Hovering the mouse over the Cluster or one of the nodes in the legend will also make the corresponding node bold in the graph."
You can read more about it in the official documentation:
User Guide - Historical Statistics of a Component
I have some sample data that I've been loading into Google BigQueries. I have been importing the data in ndjson format. If I load the data all in one file, I see them show up in a different order in the table's preview tab than when I sequentially import them one ndjson line at a time.
When importing sequentially I wait till I see the following output:
Waiting on bqjob_XXXX ... (2s) Current status: RUNNING
Waiting on bqjob_XXXX ... (2s) Current status: DONE
The order the rows show up seems to match the order I append them as the job importing them seem to finish before I move on to the next. But when loading them all in one file, they show up in a different order than they exist in my data file.
So why do the data entries show up in a different order when loading in bulk? How are the data entries queued to be loaded and also how are they indexed into the table?
BigQuery has no notion of indexes. Data in BigQuery tables have no particular order that you can rely on. If you need to get ordered data out of BigQuery you will need to use explicit ORDER BY in your query - which btw quite not recommended for large results as it increases resource cost and can end up with Resources Exceeded error.
BigQuery internal storage can "shuffle" your data rows internally for the best / most optimal performance of querying. So again - there is no such things as physical order of data in BigQuery tables
Oficial language in docs is like this - line ordering is not guaranteed for compressed or uncompressed files.
I was hoping for some help:
I have 200 participants, each with their own GPS file. Each GPS file has 1000s of points, one for every 15 second epoch. I have already cleaned the data through PALMS and have brought the file into arcmap. I would like to merge multiple points that share the same location and trip mode type (e.g., stationary, location number 1, see table attached). The main difference is that the merging needs to be sensitive to time (e.g., there might be 4 points at location 1 as that person visited the location on 4 separate occasions). I don't have tracking analyst, so any help would be much appreciated! The end game is that I need to create 1km anchor points based on places where the track stayed in one location for a large amount of time (e.g., a minimum of two hours, at least three times per week). Could someone suggest how I might do this?
Thanking you.
Erika
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