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Summary
I have a simple power BI report with 3 KPIs that take 0.3 seconds to refresh. Once I move the data to Azure Dedicated SQL Pool, same report takes 6 seconds to refresh. The goal is to have a report that would load in less than 5 seconds with 10-15 KPIs and up to 10 filters applied and connect to much larger (several million lines) datasets.
How to improve performance of Power BI connected via Direct Query to Azure Dedicated SQL pool?
More details
Setup: Dedicated SQL pool > Power BI (Direct Query)
Data: 3 tables (rows x columns: 4x136 user table, 4x3000 date table, 8x270.000 main table)
Data model: Main table to Date table connection - many-to-one based on “date” type field. Main to User connection - many-to-one based on user_id (varchar(10)). Other data types used: date, integers, varchars (50 and less).
Dedicated SQL pool performance level is Gen2: DW200c. However, the number of DWUs used never exceeds 50, most often it stays under 15.
Report:
The report includes 3 KPIs that are calculated based on the same formula (just with “5 symbols text” being different):
KPI 1:=
CALCULATE(
COUNTA('Table Main'[id]),
CONTAINSSTRING('Table Main'[varchar 50 field 0], "5 symbols text"),
ISBLANK('Table Main' [varchar 50 field 1]),
ISBLANK('Table Main' [varchar 50 field 2])
)
The report includes 6 filters: 5 “basic filtering” with 1 or several values chosen, 1 “string contains” filter.
Problem:
The visual displaying 3 KPIs (just 3 numbers) takes 6…8 seconds to load, which is a lot, especially given the need to add more KPIs and filters. (compared to 0,3 seconds if all data is loaded in .pbix file). It is a problem because adding more KPIs on the page (10-15) increases the time to load proportionally or even more for KPI that are more complicated to calculate and reports become unusable.
Question:
Is it possible to significantly improve the performance of the report/AAS/SQL pool (2…10 times faster)? How?
If not, is it possible to somehow cache the calculated KPIs / visual contents in the report or AAS without querying the data every time and without keeping the data in pbix or AAS model?
Solutions tried and not working:
Sole use and different combinations of clustered columnstore, clustered rowstore, non-clustered rowstore indexes. Automated statistics on/off. Automated indexes and stats do give an improvement of 10…20%, but it is definitely not enough.
Simple list of values (1 column from any table) takes 1.5 to 4 seconds to load)
What I have tried
Moving SQL pool from West Europe to France and back. No improvements
Applying indexes: row- and columnstore, clustered and non-clustered, manual and automatically defined (inl. statistics) - gives 10...20% improvements in performance, which does not resolve the issue.
Changing resource classes: smallrc, largerc, xlargerc. % of DWUs used still never exceeds 50 (out of 200). No improvements
Shrinking data formats and removing excessive data: minimal nvarchar(n) possible, the biggest is nvarchar (50), all excessive columns have been removed. No improvements
Isolating the model: I have a larger data model, for testing puproses I have isolated 3 tables into a separate model to make sure other parts do not affect the performance. No improvements.
Reducing number of KPI and filters
With only 2 report filters left (main table fields only) the visual takes 2 seconds to load. With +2 filters on the connected date table 2,5 sec, with +2 filters on user table 6 sec. Which means that I would only be able to use 1-2 filter reports, which is not acceptable.
It's a bit of trial and error process unfortunately. Here are a few things that are not in your list already:
many-to-one based on user_id (varchar(10)) -> Add a numeric column which is a hash of user_id column and use that to join instead of varchar column.
Ensure your statistics are up to date.
Try Dual mode. Load smaller dimension tables in-memory and keep fact tables in DB.
Use aggregates such that unless a user is trying to drill down report is actually populated without querying DB.
Partition your fact table by appropriate column.
Make sure you're using the right distribution for your fact table and have chosen the right column.
Be careful with Partitioning and Distribution. Synapse is designed a little differently than traditional RDBs (like MySQL), it's closer to NoSQL DBs to some extent, but not completely. So understand how these concepts work in Syanpse before using them (or your might get worse performance)!
I'm working on a dataset, which has as columns points in time (e.g. August, September, etc.) and as rows different measurements which were collected at that point.
Apart from that, the data is not clean at all, the are a lot of missing data and I just can't drop all the rows with them or filling them up so my idea was to divide the dataset in 4 smaller ones.
What kind of analysis can be performed on a dataset of this kind? Should I invert columns and rows?
A timeseries regression with missing data is a special case within statistical analysis. Simply re-jigging the data set is not the solution.
I understand periodicity analysis and spectral analysis is performed to identify the sinosoid of best fit, i.e. a sine wave is driven through the missing data points and regression is one approach in identifying the fit to the existing data.
The same question has been previously raised on Stats exchange based on ARIMA (moving average). Personally, I am not overawed by this approach because there will be a specialist solution.
https://stats.stackexchange.com/questions/121414/how-do-i-handle-nonexistent-or-missing-data
For a project, I have to deal with many sensors Time Series data.
I have an industrial machine that produces some artifacts. For each work (max 20 mins in time) sensors record oil pressure and temperature, and some other vibrational data (very high frequencies). All these Time Series are recorded in a .csv file, one for each sensor and for each work. Each file is named:
yyyy_mm_dd_hh_mm_ss_sensorname.csv
and contains just a sequence of real numbers.
I have to store somehow this kind of data. I am benchmarking many solution, relational and not, like MySQL, Cassandra, Mongo, etc.
In particular, for Cassandra and Mongo, I am using Pentaho Data Integration as ETL tool.
I have designed a common scheme for both DBs (unique column family/collection):
---------------------------------------
id | value | timestamp | sensor | order
---------------------------------------
The problem is that I am forced to extract timestamp and sensor information from filenames, and I have to apply many transformation to have the desired formats.
This slows my whole job down: uploading a single work (with just a single high-frequency metric, for a total of 3M rows, more or less) takes 3 mins for MongoDB, 8 mins for Cassandra.
I am running both DBs on a single node (for now), with 16 GB RAM and an 15 Core CPU.
I am sure I am doing the transformation wrong, so the question is: how can I speed things up??
Here is my KTR file: https://imgur.com/a/UZu4kYv (not enough rep to post images)
You cannot unfortunately use the filename which is on the Additional output field tab because this field is populated in parallel and there are chance it is not known when you use it in computations.
However, in your case, you can put the filename in a field, for example with a data grid, and use it for computations of timestamp and sensor. In parallel, you make the needed transforms on id, value and order. When finished you put them together again. I added a Unique Row on the common flow, just in case the input is buggy and have more than one timestamp, sensor.
I am trying to come up with a theoretical solution to an NxN problem for data aggregation and storage. As an example I have a huge amount of data that comes in via a stream. The stream sends the data in points. Each point has 5 dimensions:
Location
Date
Time
Name
Statistics
This data then needs to be aggregated and stored to allow another user to come along and query the data for both location and time. The user should be able to query like the following (pseudo-code):
Show me aggregated statistics for Location 1,2,3,4,....N between Dates 01/01/2011 and 01/03/2011 between times 11am and 4pm
Unfortunately due to the scale of the data it is not possible to aggregate all this data from the points on the fly and so aggregation prior to this needs to be done. As you can see though there are multiple dimensions that the data could be aggregated on.
They can query for any number of days or locations and so finding all the combinations would require huge pre-aggregation:
Record for Locations 1 Today
Record for Locations 1,2 Today
Record for Locations 1,3 Today
Record for Locations 1,2,3 Today
etc... up to N
Preprocessing all of these combinations prior to querying could result in an amount of precessing that is not viable. If we have 200 different locations then we have 2^200 combinations which would be nearly impossible to precompute in any reasonable amount of time.
I did think about creating records on 1 dimension and then merging could be done on the fly when requested, but this would also take time at scale.
Questions:
How should I go about choosing the right dimension and/or combination of dimensions given that the user is as likely to query on all dimensions?
Are there any case studies I could refer to, books I could read or anything else you can think of that would help?
Thank you for your time.
EDIT 1
When I say aggregating the data together I mean combining the statistics and name (dimensions 4 & 5) for the other dimensions. So for example if I request data for Locations 1,2,3,4..N then I must merge the statistics and counts of name together for those N Locations before serving it up to the user.
Similarly if I request the data for dates 01/01/2015 - 01/12/2015 then I must aggregate all data between those periods (by adding summing name/statistics).
Finally If I ask for data between dates 01/01/2015 - 01/12/2015 for Locations 1,2,3,4..N then I must aggregate all data between those dates for all those locations.
For the sake of this example lets say that going through statistics requires some sort of nested loop and does not scale well especially on the fly.
Try a time-series database!
From your description it seems that your data is a time-series dataset.
The user seems to be mostly concerned about the time when querying and after selecting a time frame, the user will refine the results by additional conditions.
With this in mind, I suggest you to try a time-series database like InfluxDB or OpenTSD.
For example, Influx provides a query language that is capable of handling queries like the following, which comes quite close to what you are trying to achieve:
SELECT count(location) FROM events
WHERE time > '2013-08-12 22:32:01.232' AND time < '2013-08-13'
GROUP BY time(10m);
I am not sure what you mean by scale, but the time-series DBs have been designed to be fast for lots of data points.
I'd suggest to definitely give them a try before rolling your own solution!
Denormalization is a means of addressing performance or scalability in relational database.
IMO having some new tables to hold aggregated data and using them for reporting will help you.
I have a huge amount of data that comes in via a stream. The stream
sends the data in points.
There will be multiple ways to achieve denormalization in the case:
Adding a new parallel endpoint for data aggregation functionality in streaming
level
Scheduling a job to aggregate data in DBMS level.
Using DBMS triggering mechanism (less efficient)
In an ideal scenario when a message reaches the streaming level there will be two copies of data message containing location, date, time, name, statistics dimensions, being dispatched for processing, one goes for OLTP(current application logic) second will goes for an OLAP(BI) process.
The BI process will create denormalized aggregated structures for reporting.
I will suggest having aggregated data record per location, date group.
So end-user will query preprossed data that wont need heavy recalculations, having some acceptable inaccuracy.
How should I go about choosing the right dimension and/or combination
of dimensions given that the user is as likely to query on all
dimensions?
That will depends on your application logic. If possible limit the user for predefined queries that can be assigned values by the user(like for dates from 01/01/2015 to 01/12/2015). In more complex systems using a report generator above the BI warehouse will be an option.
I'd recommend Kimball's The Data Warehouse ETL Toolkit.
You can at least reduce Date and Time to a single dimension, and pre-aggregate your data based on your minimum granularity, e.g. 1-second or 1-minute resolution. It could be useful to cache and chunk your incoming stream for the same resolution, e.g. append totals to the datastore every second instead of updating for every point.
What's the size and likelyhood of change of the name and location domains? Is there any relation between them? You said that location could be as many as 200. I'm thinking that if name is a very small set and unlikely to change, you could hold counts of names in per-name columns in a single record, reducing the scale of the table to 1 row per location per unit of time.
you have a lot of datas. It will take a lot of time with all methods due to the amount of datas you're trying to parse.
I have two methods to give.
First one is a brutal one, you probably thought off:
id | location | date | time | name | statistics
0 | blablabl | blab | blbl | blab | blablablab
1 | blablabl | blab | blbl | blab | blablablab
ect.
With this one, you can easily parse and get elements, they are all in the same table, but the parsing is long and the table is enormous.
Second one is better I think:
Multiple tables:
id | location
0 | blablabl
id | date
0 | blab
id | time
0 | blab
id | name
0 | blab
id | statistics
0 | blablablab
With this you could parse (a lot) faster, getting the IDs and then taking all the needed informations.
It also allow you to preparse all the datas:
You can have the locations sorted by location, the time sorted by time, the name sorted by alphabet, ect, because we don't care about how the ID's are mixed:
If the id's are 1 2 3 or 1 3 2, no one actually care, and you would go a lot faster with parsing if your datas are already parsed in their respective tables.
So, if you use the second method I gave: At the moment where you receive a point of data, give an ID to each of his columns:
You receive:
London 12/12/12 02:23:32 donut verygoodstatsblablabla
You add the ID to each part of this and go parse them in their respective columns:
42 | London ==> goes with London location in the location table
42 | 12/12/12 ==> goes with 12/12/12 dates in the date table
42 | ...
With this, you want to get all the London datas, they are all side by side, you just have to take all the ids, and get the other datas with them. If you want to take all the datas between 11/11/11 and 12/12/12, they are all side by side, you just have to take the ids ect..
Hope I helped, sorry for my poor english.
You should check out Apache Flume and Hadoop
http://hortonworks.com/hadoop/flume/#tutorials
The flume agent can be used to capture and aggregate the data into HDFS, and you can scale this as needed. Once it is in HDFS there are many options to visualize and even use map reduce or elastic search to view the data sets you are looking for in the examples provided.
I have worked with a point-of-sale database with hundred thousand products and ten thousand stores (typically week-level aggregated sales but also receipt-level stuff for basket analysis, cross sales etc.). I would suggest you to have a look at these:
Amazon Redshift, highly scalable and relatively simple to get started, cost-efficient
Microsoft Columnstore Indexes, compresses data and has familiar SQL interface, quite expensive (1 year reserved instance r3.2xlarge at AWS is about 37.000 USD), no experience on how it scales within a cluster
ElasticSearch is my personal favourite, highly scalable, very efficient searches via inverted indexes, nice aggregation framework, no license fees, has its own query language but simple queries are simple to express
In my experiments ElasticSearch was faster than Microsoft's column store or clustered index tables for small and medium-size queries by 20 - 50% on same hardware. To have fast response times you must have sufficient amount of RAM to have necessary data structures loaded in-memory.
I know I'm missing many other DB engines and platforms but I am most familiar with these. I have also used Apache Spark but not in data aggregation context but for distributed mathematical model training.
Is there really likely to be a way of doing this without brute forcing it in some way?
I'm only familiar with relational databases, and I think that the only real way to tackle this is with a flat table as suggested before i.e. all your datapoints as fields in a single table. I guess that you just have to decide how to do this, and how to optimize it.
Unless you have to maintain 100% to the single record accuracy, then I think the question really needs to be, what can we throw away.
I think my approach would be to:
Work out what the smallest time fragment would be and quantise the time domain on that. e.g. each analyseable record is 15 minutes long.
Collect raw records together into a raw table as they come in, but as the quantising window passes, summarize the rows into the analytical table (for the 15 minute window).
Deletion of old raw records can be done by a less time-sensitive routine.
Location looks like a restricted set, so use a table to convert these to integers.
Index all the columns in the summary table.
Run queries.
Obviously I'm betting that quantising the time domain in this way is acceptable. You could supply interactive drill-down by querying back onto the raw data by time domain too, but that would still be slow.
Hope this helps.
Mark
I have a dataset (where each data is a vector of attributes with their corresponding class label). I want to split the dataset to a training set and testing set. Is there anyway to do this automatically ?
The typical way to split a modeling data set into training, validation, and test sets is to use a random sample. That is, assign a random number between 0 and 1. If you want a 40/30/30 split, then rows where the value is between 0 and 0.4 are in training, between 0.4 and 0.7 in validatino, and 0.7 and 1.0 in test. There is nothing magical about the 40/30/30 slpit. It just happens to be the default in SAS Enterprise Miner (in fact, I often change it to 60/30/10).
There are some tweaks and possible improvements to this. If you know there are important features for modeling, such as geography, then you can do a stratified sample. For this, you would sort the data by the columns and then essentially do an "every nth" record sample. I say "essentially", because this is a little more complicated for a 40% split. To handle this, take the record 10 at a time, choose 4 for the training set, 3 for the validation set, and 3 for the test set. Which particfular ones you choose from the 10 are not important.
A bigger issue is when you have hierarchical data, and virtually all data used for modeling is hierarchies. For instance, you might have customer data with a significant number of columns describing the customer's census tract. If these variables are important as predictors, then you might consider sampling at the census tract level, rather than the customer level. That is, divide the census tracts into three groups (randomly), so 40% of customers go to the training set, 30% to the validation set, and 30% to the test set.
You want to be sure that you partition the data, and no record falls into more than one group. If you don't know what training, validation, and test sets are, then I would highly recommend that you get a book on data mining (such as "Data Mining Techniques for Marketing, Sales, and Customer Support, Third Edition" at http://www.amazon.com/Data-Mining-Techniques-Relationship-Management/dp/0470650931/ref=pd_sim_b_5).