I have an AS tabular model that contains a fact table with 20 mil rows. I have partitioned this so only the new rows get added to each day... however occasionally, a historical row (from years ago) will be modified. I can identify this modified row in SQL (using the last modified timestamp) however would it be possible for me to refresh the row in SSAS to reflect this change without having to refresh my entire data model? How would I achieve this?
First, 20 million rows is not a lot. I’m expecting that will only take 5-10 minutes to process unless your SQL queries are very inefficient or very wide. So why bother to optimize something which may be fast enough already?
If you do need to optimize it, you will first want to partition the large fact table by some date element. Since you only have 20 million rows I would suggest partitioning by year. Optimal compression will be achieved with around 8 million rows per partition. Over-partitioning (such as creating thousands of daily partitions) is counter-productive.
When a new row is added you could perform a ProcessAdd to insert just the new records to the partitions in question. However I would recommend just doing a ProcessFull on any year partitions which have any inserts, updates or deletes in SQL.
SSAS doesn’t support updating a specific row. Thus you have to follow the ProcessFull advice above.
There are several code examples including this one which may help you.
Again this may be overkill if you only have 20 million rows.
I am trying to Update a huge table in Teradata on a daily basis. The Update Statement is taking a lot of AMPCPUTime.
Table contains 65 Billion rows and 100-200 Million Rows are updated.
Table is a Set Table with Non Unique PI. The data distribution is quite even with 0.8 Skew Factor.
What is the way to reduce the AMPCPU Time?
The Update is done using a Stage table. Join is on a subset of PI columns.
Attempts: Changed the PI of stage table same as Target Table. Explain PLan says a Merge Update is being performed. But AMPCPUTime is rather increasing.
Tried Delete and Insert but Delete and Insert also taking greater AMPCPUTime.
First, I explain my problem:
This is a table that will contain approximately 5,000,000 record per year, these records will be kept at least 10 years (it is not yet defined). We talk about events of production machine. I generate a report + a dashbord for displaying various information relatively complex (average number of events per 10 minutes a month, graphics, ...) and also wants to see the records themselves. The data displayed will be in large majority of the last 2 months, viewing the rest of the data must always be possible but at a lower speed of access.
I work on MariaDB v10.1.12.
The idea was to make a partition on the last 3 months. I realize now that this is not so easy. I have not found any solution to this partition, in fact, it is impossible to make a partition based on a now() or other current_date() etc. directly or indirectly via another calculated column.
Do you have any ideas for me? Perhaps another solution than a partition.
Thank you in advance.
I recommend PARTITION BY RANGE(TO_DAYS(...)) If you are only now breaking the table into partitions, I would recommend annual partitions for data before this year, then quarterly or monthly partitions henceforth. Yes, that, in theory, leads to an infinite number of partitions, but I predict that you will revamp the data structure within a few years.
20-50 partitions is a good number. More than that leads to inefficiencies due to the multitude of partitions; less than that leads to asking "why bother".
Use InnoDB. Design the PRIMARY KEY carefully, since it may be useful as the primary index into the data.
Usually it is best to put the date/timestamp column last in any indexes. Putting it first would be redundant since partition pruning comes first.
More on partitioning.
It sounds like a main purpose for the table is to summarize the data for graphing, etc. In that case, it may be very beneficial to build and maintain "Summary table(s)" of counts and subtotals over selected time intervals. 100 rows get added up for a 10-minute interval? If so, then the summary table based on 10-minute intervals would have 1/100th as many rows, and the queries would be much faster. Plus, you could 'denormalize' the summary tables to make them even simpler.
More on Summary tables.
It might be worth it to gather data for 10 minutes into a staging table, then summarize it into the summary table. And also throw the raw data into the big table.
Or, if the summary tables have everything you need, you could abandon the big table. Or, as a compromise, keep 12 month's worth of data (partitioned by month), and DROP PARTITION for older data. Meanwhile, the summary tables can continue to grow (although they will be much smaller).
Table partitioning is an advance features, it is not indexing, but rearrangement of tables data. So it is not "duplicate", indeed new data will stored according to the predefined partitioning range.
You must also specify month range criteria as usual. you MUST create index if those column are not used as partition range. When you make a select, algorithm that associate with partition table will handle those merging(if required) in background. So you just treat partition exactly like your typical table.
For more details, please check Mariadb paritioning overview
I output the query plan on SQLite, and it shows
0|0|0|SCAN TABLE t (~500000 rows)
I wonder what is the meaning of the number (500000)? I guess it is the table length, but I executed the query on a small table which does not have so many rows.
Is there any official document about the meaning of the number? thanks.
As the official documentation says, this is the number of rows that the database estimates will be returned.
If there is an index on a seached column, and if you have run ANALYZE, then SQLite can make an estimate based on the actual data. Otherwise, it assumes that tables contain one million rows, and that a search like column > x filters out half the rows.
I want to insert 150K records from a source table into a destination table. The problem is that i have to calculate some values for the destination table too.
How should i use the bulk collect and for statement for the INSERT DML.
Please find the elaborate explanation below.
Source Table
Account_id | Status1| Status2
Table 1
Account_id | column2| column3| column4|column6|column7
Table 2
Account_id | column2| column3| column6|column9|column10
NOw I have to fetch the values from table 1 for the account_ids matching the source table and insert into table 2 where i have to populate column9 and column 10 dynamically.
BULK COLLECT requires a lot of memory and in practice is only feasible if you process your data in chunks, i.e. about 1000 rows at a time. Otherwise the memory consumption will be too much for most systems.
The best option is usually to create a single INSERT .. SELECT statement that retrieves, calculates and inserts all data at once.
If this is not possible or far too complex, the second best option in my opinion is a pipelined function written in PL/SQL.
The third best and usually easiest option is a simple PL/SQL loop that select row by row, calculates the required data and inserts it row by row. Performance wise it's usually the worst. But it can be still more than sufficient.
For more precise answers, you need to specify the exact problem at hand. Your question is rather broad.