I have successfully used combination of crossfilter, dc, d3 to build multivariate charts for smaller datasets.
My current system caters to 1.5 million txns a day and I want to use the above combination to show dimensional charts on this big sized data (spanned over 6 months). I cannot push this sized data to the frontend for obvious reasons.
The txn data has seconds level granularity but this level of granularity is not required in the visualization. If txn data can be rolled up to a granularity of a day at the backend and push the day based aggregation to the front end then it can drastically reduce the IO traffic and size of the data given to the crossfilter,dc and then dc can show its visualization magic.
Taking forward the above idea -> I decided to reduce the size of the data by reducing the granularity of the timeseries data from millseconds to day by pre-aggregating the data from various dimensions using the below GROUP BY query (this is similar to the stuff done by crossfilter but at the frontend)
SELECT TRUNC(DATELOGGED) AS DTLOGGED, CODE, ACTION, COUNT(*) AS
TXNCOUNT, GROUPING_ID(TRUNC(DATELOGGED),CODE, ACTION) AS grouping_id
FROM AAAA GROUP BY GROUPING SETS(TRUNC(DATELOGGED),
(TRUNC(DATELOGGED),CURR_CODE), (TRUNC(DATELOGGED),ACTION));
Sample output of these rows:
Tuples/Rows in which aggregation is done by (TRUNC(DATELOGGED),CODE) will have a common grouping_id 1 and by (TRUNC(DATELOGGED),ACTION) will have a common grouping_id 2
//group by DTLOGGED, CODE
{"DTLOGGED":"2013-08-03T07:00:00.000Z","CODE":"144","ACTION":"", "TXNCOUNT":69,"GROUPING_ID":1},
{"DTLOGGED":"2013-08-03T07:00:00.000Z","CODE":"376","ACTION":"", "TXNCOUNT":20,"GROUPING_ID":1},
{"DTLOGGED":"2013-08-04T07:00:00.000Z","CODE":"144","ACTION":"", "TXNCOUNT":254,"GROUPING_ID":1},
{"DTLOGGED":"2013-08-04T07:00:00.000Z","CODE":"376","ACTION":"", "TXNCOUNT":961,"GROUPING_ID":1},
//group by DTLOGGED, ACTION
{"DTLOGGED":"2013-08-03T07:00:00.000Z","CODE":"","ACTION":"ENROLLED_PURCHASE", "TXNCOUNT":373600,"GROUPING_ID":2},
{"DTLOGGED":"2013-08-03T07:00:00.000Z","CODE":"","ACTION":"UNENROLLED_PURCHASE", "TXNCOUNT":48978,"GROUPING_ID":2},
{"DTLOGGED":"2013-08-04T07:00:00.000Z","CODE":"","ACTION":"ENROLLED_PURCHASE", "TXNCOUNT":402311,"GROUPING_ID":2},
{"DTLOGGED":"2013-08-04T07:00:00.000Z","CODE":"","ACTION":"UNENROLLED_PURCHASE", "TXNCOUNT":54910,"GROUPING_ID":2},
//group by DTLOGGED
{"DTLOGGED":"2013-08-03T07:00:00.000Z","CODE":"","ACTION":"", "TXNCOUNT":460732,"GROUPING_ID":3},
{"DTLOGGED":"2013-08-04T07:00:00.000Z","CODE":"","ACTION":"", "TXNCOUNT":496060,"GROUPING_ID":3}];
Questions:
These rows are are dis-joined i.e. not like usual rows where each row will have valid values for CODE and ACTION in a single row.
After a selection is made in one of the graphs, the redrawing effect either removes the other graphs or shows no data on them.
Please give me any troubleshooting help or suggest better ways to solve this?
http://jsfiddle.net/universallocalhost/5qJjT/3/
So there are a couple things going on in this question, so I'll try to separate them:
Crossfilter works with tidy data
http://vita.had.co.nz/papers/tidy-data.pdf
This means that you will need to come up with a naive method of filling in the nulls you're seeing (or if need be, in your initial query of the data, omit the nulled values. If you want to get really fancy, you could even infer the null values based off of other data. Whatever your solution, you need to make your data tidy prior to putting it into crossfilter.
Groups and Filtering Operations
txnVolByCurrcode = txnByCurrcode.group().reduceSum(function(d) {
if(d.GROUPING_ID ===1) {
return d.TXNCOUNT;
} else {
return 0;
}
});
This is a filtering operation done on the reduction. This is something that you should separate. Allow that filtering to occur elsewhere (either in the visual, crossfilter itself, or in the query on the data).
This means your reduceSum's become:
var txnVolByCurrcode = txnByCurrcode.group().reduceSum(function(d) {
return d.TXNCOUNT;
});
And if you would like the user to select which group to display:
var groupId = cfdata.dimension(function(d) { return d.GROUPING_ID; });
var groupIdGroup = groupId.group(); // this is an interesting name
dc.pieChart("#group-chart")
.width(250)
.height(250)
.radius(125)
.innerRadius(50)
.transitionDuration(750)
.dimension(groupId)
.group(groupIdGroup)
.renderLabel(true);
For an example of this working:
http://jsfiddle.net/b67pX/
Related
I have data in the form:
data = [..., {id:X,..., turnover:[[2015,2017,2018],[2000000,3000000,2800000]]}, ...];
My goal is to plot the year in the x-axis, against the average turnover for all companies currently selected via crossfilter in the y-axis.
The years recorded per company are inconsistent, but there should always be three years.
If it would help, I can reorganise the data to be in the form:
data = [..., {id:X,..., turnover:{2015:2000000, 2017:3000000, 2018:2800000}}, ...];
Had I been able to reorganise the data further to look like:
[...{id:X, ..., year:2015, turnover:2000000},{id:X,...,year:2017,turnover:3000000},{id:X,...,year:2018,turnover:2800000}];
Then this question would provide a solution.
But splitting the companies into separate rows doesn't make sense with everything else I'm doing.
Unless I'm mistaken, you have what I call a "tag dimension", aka a dimension with array keys.
You want each row to be recorded once for each year it contains, but you only want it to affect this dimension. You don't want to observe the row multiple times in the other dimensions, which is why you don't want to flatten.
With your original data format, your dimension definition would look something like:
var yearsDimension = cf.dimension(d => d.turnover[0], true);
The key function for a tag dimension should return an array, here of years.
This feature is still fairly new, as crossfilter goes, and a couple of minor bugs were found this year. These bugs should be easy to avoid. The feature has gotten a lot of use and no major bugs have been found.
Always beware with tag dimensions, since any aggregations will add up to more than 100% - in your case 300%. But if you are doing averages across companies for a year, this should not be a problem.
pairs of tags and values
What's unique about your problem is that you not only have multiple keys per row, you also have multiple values associated with those keys.
Although the crossfilter tag dimension feature is handy, it gives you no way to know which tag you are looking at when you reduce. Further, the most powerful and general group reduction method, group.reduce(), doesn't tell you which key you are reducing..
But there is one even more powerful way to reduce across the entire crossfilter at once: dimension.groupAll()
A groupAll object behaves like a group, except that it is fed all of the rows, and it returns only one bin. If you use dimension.groupAll() you get a groupAll object that observes all filters except those on that dimension. You can also use crossfilter.groupAll if you want a groupAll that observes all filters.
Here is a solution (using ES6 syntax for brevity) of reduction functions for groupAll.reduce() that reduces all of the rows into an object of year => {count, total}.
function avg_paired_tag_reduction(idTag, valTag) {
return {
add(p, v) {
v[idTag].forEach((id, i) => {
p[id] = p[id] || {count: 0, total: 0};
++p[id].count;
p[id].total += v[valTag][i];
});
return p;
},
remove(p, v) {
v[idTag].forEach((id, i) => {
console.assert(p[id]);
--p[id].count;
p[id].total -= v[valTag][i];
})
return p;
},
init() {
return {};
}
};
}
It will be fed every row and it will loop over the keys and values in the row, producing a count and total for every key. It assumes that the length of the key array and the value array are the same.
Then we can use a "fake group" to turn the object on demand into the array of {key,value} pairs that dc.js charts expect:
function groupall_map_to_group(groupAll) {
return {
all() {
return Object.entries(groupAll.value())
.map(([key, value]) => ({key,value}));
}
};
}
Use these functions like this:
const red = avg_paired_tag_reduction('id', 'val');
const avgPairedTagGroup = turnoverYearsDim.groupAll().reduce(
red.add, red.remove, red.init
);
console.log(groupall_map_to_group(avgPairedTagGroup).all());
Although it's possible to compute a running average, it's more efficient to instead calculate a count and total, as above, and then tell the chart how to compute the average in the value accessor:
chart.dimension(turnoverYearsDim)
.group(groupall_map_to_group(avgPairedTagGroup))
.valueAccessor(kv => kv.value.total / kv.value.count)
Demo fiddle.
I have a table with 5 categories and units displayed into 2 types, Actual and budget.
I want to filter this table. Only when 2 or more values are selected in the slicer. Something like this.
I though of adding a measure, but dont know how to work the if statement exactly.
Measure = IF(COUNTROWS(ALLSELECTED(Report[Shipment Group])) = 1, "Something which would not filter the units", SELECTEDVALUE(Report[Units], SUM(Report[Units])))
Not sure if this is correct approach.Would like to know if any other approach is possible. Any help would be helpful. Thank you in advance.
This is a bit of an odd request, but I think I have something that works.
First, you need to create a separate table for your slicer values (or else you can't control filtering how you want). You can hit the new table button and define it as follows:
Groups = VALUES(Report[Shipment Group])
Set your slicer to use Groups[Shipment Group] instead of Report[Shipment Group].
Define your new measure as follows:
Measure = IF(COUNTROWS(ALLSELECTED(Groups[Shipment Group])) = 1,
SUM(Report[Units]),
SUMX(FILTER(Report,
Report[Shipment Group] IN VALUES(Groups[Shipment Group])),
Report[Units]))
or equivalently
Measure = IF(COUNTROWS(ALLSELECTED(Groups[Shipment Group])) = 1,
SUM(Report[Units]),
CALCULATE(SUM(Report[Units]),
FILTER(Report,
Report[Shipment Group] IN VALUES(Groups[Shipment Group]))))
Note: Double check that Power BI has not automatically created a relationship between the Groups and Report tables. You don't want that.
I would like to store user purchase custom tags on each transaction, example if user bought shoes then tags are "SPORTS", "NIKE", SHOES, COLOUR_BLACK, SIZE_12,..
These tags are that seller interested in querying back to understand the sales.
My idea is when ever new tag comes in create new code(something like hashcode but sequential) for that tag, and code starts from "a-z" 26 letters then "aa, ab, ac...zz" goes on. Now keep all the tags given for in one transaction in the one column called tag (varchar) by separating with "|".
Let us assume mapping is (at application level)
"SPORTS" = a
"TENNIS" = b
"CRICKET" = c
...
...
"NIKE" = z //Brands company
"ADIDAS" = aa
"WOODLAND" = ab
...
...
SHOES = ay
...
...
COLOUR_BLACK = bc
COLOUR_RED = bd
COLOUR_BLUE = be
...
SIZE_12 = cq
...
So storing the above purchase transaction, tag will be like tag="|a|z|ay|bc|cq|" And now allowing seller to search number of SHOES sold by adding WHERE condition tag LIKE %|ay|%. Now the problem is i cannot use index (sort key in redshift db) for "LIKE starts with %". So how to solve this issue, since i might have 100 millions of records? dont want full table scan..
any solution to fix this?
Update_1:
I have not followed bridge table concept (cross-reference table) since I want to perform group by on the results after searching the specified tags. My solution will give only one row when two tags matched in a single transaction, but bridge table will give me two rows? then my sum() will be doubled.
I got suggestion like below
EXISTS (SELECT 1 FROM transaction_tag WHERE tag_id = 'zz' and trans_id
= tr.trans_id) in the WHERE clause once for each tag (note: assumes tr is an alias to the transaction table in the surrounding query)
I have not followed this; since i have to perform AND and OR condition on the tags, example ("SPORTS" AND "ADIDAS") ---- "SHOE" AND ("NIKE" OR "ADIDAS")
Update_2:
I have not followed bitfield, since dont know redshift has this support also I assuming if my system will be going to have minimum of 3500 tags, and allocating one bit for each; which results in 437 bytes for each transaction, though there will be only max of 5 tags can be given for a transaction. Any optimisation here?
Solution_1:
I have thought of adding min (SMALL_INT) and max value (SMALL_INT) along with tags column, and apply index on that.
so something like this
"SPORTS" = a = 1
"TENNIS" = b = 2
"CRICKET" = c = 3
...
...
"NIKE" = z = 26
"ADIDAS" = aa = 27
So my column values are
`tag="|a|z|ay|bc|cq|"` //sorted?
`minTag=1`
`maxTag=95` //for cq
And query for searching shoe(ay=51) is
maxTag <= 51 AND tag LIKE %|ay|%
And query for searching shoe(ay=51) AND SIZE_12 (cq=95) is
minTag >= 51 AND maxTag <= 95 AND tag LIKE %|ay|%|cq|%
Will this give any benefit? Kindly suggest any alternatives.
You can implement auto-tagging while the files get loaded to S3. Tagging at the DB level is too-late in the process. Tedious and involves lot of hard-coding
While loading to S3 tag it using the AWS s3API
example below
aws s3api put-object-tagging --bucket --key --tagging "TagSet=[{Key=Addidas,Value=AY}]"
capture tags dynamically by sending and as a parameter
2.load the tags to dynamodb as a metadata store
3.load data to Redshift using S3 COPY command
You can store tags column as varchar bit mask, i.e. a strictly defined bit sequence of 1s or 0s, so that if a purchase is marked by a tag there will be 1 and if not there will be 0, etc. For every row, you will have a sequence of 0s and 1s that has the same length as the number of tags you have. This sequence is sortable, however you would still need lookup into the middle but you will know at which specific position to look so you don't need like, just substring. For further optimization, you can convert this bit mask to integer values (it will be unique for each sequence) and make matching based on that but AFAIK Redshift doesn't support that yet out of box, you will have to define the rules yourself.
UPD: Looks like the best option here is to keep tags in a separate table and create an ETL process that unwraps tags into tabular structure of order_id, tag_id, distributed by order_id and sorted by tag_id. Optionally, you can create a view that joins the this one with the order table. Then lookups for orders with a particular tag and further aggregations of orders should be efficient. There is no silver bullet for optimizing this in a flat table, at least I don't know of such that would not bring a lot of unnecessary complexity versus "relational" solution.
I've read through the Crossfilter API docs several times but can't see how to do the following.
Suppose I have set up
crossfilter(event);
and a dimension foo:
var foo = event.dimension(function(d) { return d.foo; }),
foos = foo.group(function(d) { return Math.floor(d) ; });
Then, before any filters are applied, event.size() will give me the number of records in the event, and foos.size() will give me the number of distinct records in the foo dimension
Great! Now I apply some filters by sliding brushes around. event.groupAll().value() now gives me the current number of records in event that are selected. Great again.
Now how do I get the current number of distinct records in the foo dimension? I've tried many different combinations of the API primitives, but none seem to work.
Any ideas?
This should do the trick
var n = foo.top(Number.POSITIVE_INFINITY).length;
I do not have enough reputation to comment the solution proposed by Reno.
This should do the trick
var n = foo.top(Number.POSITIVE_INFINITY).length;
The problem of this solution is that is not efficient, because top function is ordering the data.
I have the same problem that you and I have a counter in the filter to know how many entries have the dimension.
Is there an efficient way to delete multiple rows in HBase or does my use case smell like not suitable for HBase?
There is a table say 'chart', which contains items that are in charts. Row keys are in the following format:
chart|date_reversed|ranked_attribute_value_reversed|content_id
Sometimes I want to regenerate chart for a given date, so I want to delete all rows starting from 'chart|date_reversed_1' till 'chart|date_reversed_2'. Is there a better way than to issue a Delete for each row found by a Scan? All the rows to be deleted are going to be close to each other.
I need to delete the rows, because I don't want one item (one content_id) to have multiple entries which it will have if its ranked_attribute_value had been changed (its change is the reason why chart needs to be regenerated).
Being a HBase beginner, so perhaps I might be misusing rows for something that columns would be better -- if you have a design suggestions, cool! Or, maybe the charts are better generated in a file (e.g. no HBase for output)? I'm using MapReduce.
Firstly, coming to the point of range delete there is no range delete yet in HBase, AFAIK. But there is a way to delete more than one rows at a time in the HTableInterface API. For this simply form a Delete object with row keys from scan and put them in a List and use the API, done! To make scan faster do not include any column family in the scan result as all you need is the row key for deleting whole rows.
Secondly, about the design. First my understanding of the requirement is, there are contents with content id and each content has charts generated against them and those data are stored; there can be multiple charts per content via dates and depends on the rank. In addition we want the last generated content's chart to show at the top of the table.
For my assumption of the requirement I would suggest using three tables - auto_id, content_charts and generated_order. The row key for content_charts would be its content id and the row key for generated_order would be a long, which would auto-decremented using HTableInterface API. For decrementing use '-1' as the amount to offset and initialize the value Long.MAX_VALUE in the auto_id table at the first start up of the app or manually. So now if you want to delete the chart data simply clean the column family using delete and then put back the new data and then make put in the generated_order table. This way the latest insertion will also be at the top in the latest insertion table which will hold the content id as a cell value. If you want to ensure generated_order has only one entry per content save the generated_order id first and take the value and save it into content_charts when putting and before deleting the column family first delete the row from generated_order. This way you could lookup and charts for a content using 2 gets at max and no scan required for the charts.
I hope this is helpful.
You can use the BulkDeleteProtocol which uses a Scan that defines the relevant range (start row, end row, filters).
See here
I ran into your situation and this is my code to implement what you want
Scan scan = new Scan();
scan.addFamily("Family");
scan.setStartRow(structuredKeyMaker.key(starDate));
scan.setStopRow(structuredKeyMaker.key(endDate + 1));
try {
ResultScanner scanner = table.getScanner(scan);
Iterator<Entity> cdrIterator = new EntityIteratorWrapper(scanner.iterator(), EntityMapper.create(); // this is a simple iterator that maps rows to exact entity of mine, not so important !
List<Delete> deletes = new ArrayList<Delete>();
int bufferSize = 10000000; // this is needed so I don't run out of memory as I have a huge amount of data ! so this is a simple in memory buffer
int counter = 0;
while (entityIterator.hasNext()) {
if (counter < bufferSize) {
// key maker is used to extract key as byte[] from my entity
deletes.add(new Delete(KeyMaker.key(entityIterator.next())));
counter++;
} else {
table.delete(deletes);
deletes.clear();
counter = 0;
}
}
if (deletes.size() > 0) {
table.delete(deletes);
deletes.clear();
}
} catch (IOException e) {
e.printStackTrace();
}