How we can identify the differnece between two nodes in Neo4j from time to time.In other words i have two graph images and my task is to identify the delta between these two..
Any suggestions?
Here i will have access to a neo4j DB which will be updating by an application to whcih i dont have accesss.And my job is to identify the changes done to DB with in a time interval like for 4 hours once etc..here nodes doesn't have any time stamps. Previously i reffered as two images since my requirement is to identify the changes happened to the same DB with in some time gap.ex 1 pm( one graph intance) to 4 pm(second graph instance) with 4 hours time interval..
Hope my query is clear..
Thanks..
Related
Below is the scenario against which I have this question.
Requirement:
Pre-aggregate time series data within influxDb with granularity of seconds, minutes, hours, days & weeks for each sensor in a device.
Current Proposal:
Create five Continuous Queries (one for each granularity level i.e. Seconds, minutes ...) for each sensor of a device in a different retention policy as that of the raw time series data, when the device is onboarded.
Limitation with Current Proposal:
With increased number of device/sensor (time series data source), the influx will get bloated with too many Continuous Queries (which is not recommended) and will take a toll on the influxDb instance itself.
Question:
To avoid the above problems, is there a possibility to create Continuous Queries on the same source measurement (i.e. raw timeseries measurement) but the aggregates can be differentiated within the measurement using new tags introduced to differentiate the results from Continuous Queries from that of the raw time series data in the measurement.
Example:
CREATE CONTINUOUS QUERY "strain_seconds" ON "database"
RESAMPLE EVERY 5s FOR 1m
BEGIN
SELECT MEAN("strain_top") AS "STRAIN_TOP_MEAN" INTO "database"."raw"."strain" FROM "database"."raw"."strain" GROUP BY time(1s),*
END
As far as I know, and have seen from the docs, it's not possible to apply new tags in continuous queries.
If I've understood the requirements correctly this is one way you could approach it.
CREATE CONTINUOUS QUERY "strain_seconds" ON "database"
RESAMPLE EVERY 5s FOR 1m
BEGIN
SELECT MEAN("strain_top") AS "STRAIN_TOP_MEAN" INTO "database"."raw"."strain" FROM "database"."strain_seconds_retention_policy"."strain" GROUP BY time(1s),*
END
This would save the data in the same measurement but a different retention policy - strain_seconds_retention_policy. When you do a select you specify the corresponding retention policy from which to select.
Note that, it is not possible to perform a select from several retention policies at the same time. If you don't specify one, the default one is used (and not all of them). If it is something you need then another approach could be used.
I don't quite get why you'd need to define a continuous query per device and per sensor. You only need to define five (1 per seconds, minutes, hours, days, weeks) and do a group by * (all) which you already do. As long as the source datapoint has a tag with the id for the corresponding device and sensor, the resampled datapoint will have it too. Any newly added devices (data) will just be processed automatically by those 5 queries and saved into the corresponding retention policies.
If you do want to apply additional tags, you could process the data outside the database in a custom script and write it back with any additional tags you need, instead of using continuous queries
is there a way to query for next 5 instances of given series? I am querying using time-frame:
1. ask for all the meetings for next 7 days for all user events
2. Go over each event fetched and check if event has masterSerieId
3. Return matching instances.
This feels (and is) a bit painful.
Can i request next X instances of master series right away? I can't just simply or 'simply' get them based on recurrence rule, as some might have expired.
I could image I could ask for a year ahead and pass as a query parameter masterSeriesId and limit output with $top. Is that right approach?
Based on this document, you could call /events//instances – given a start time, returns all instances in the requested time frame of the meeting specified by the provided series master ID
We need to get the daily data from the "Microsoft.VSTS.Scheduling.CompletedWork"field (which is detailed in Workload, scheduling and time tracking field references). However I get data from the Analysis database and found that it only records one last new data,and can't get the historical data.
For example the task of ID 3356, who's "CompletedWork" is 3 hours in 2016/8/4, and I get the exact 3 hours-data from the Analysis database in the second day, 2016/8/5, as the pictures in this post show.
Then on the 2016/8/5, I update the "CompletedWork" from 3 hours to 4 hours and I get the exact 4 hours-data from the Analysis database in the second day, 2016/8/6. However the 3 hours-data of 2016/8/4 is lost. Well, How can I get the historical data of "Microsoft.VSTS.Scheduling.CompletedWork"?
First of all, it's important to understand that the CompletedWork is a cumulatieve data field. So when one user enters 3 and another enters 4, the total number of hours worked on the field is 4 not 7.
The warehouse has a granularity of a day and keeps that data int he cube, though the relational warehouse tables will store all the changes to the reportable fields on a per-revision bases. You can't easily query this data using the qube or Excel Power Pivot and they're lost in the Dim* and fact* tables, but you can write a SQL query against tfs_warehouse and iterate through the tables containing the workitem data (tbl_workitems[are|were|latest]). This is much slower and much harder to build unfortunately.
Your other alternative is to use the TFS Client Object Model and query the WorkItemStore object directly. You'll be able to query all work items of interest and iterate through them and their revisions. The API for workitems is relatively easy to use and is well documented.
If you're on TFS 2015 you can also use the new REST api to query workitem data and revisions.
I want to answer the question: what time window my servers are least used?
My idea is to have a 24h histogram (with 10min buckets) with the count of requests made to my server. In other words, I want to ignore date portion in datetime aggregations.
How do I create such graph in Kibana 4?
For my case, I need to capture 15 performance metrics for devices and save it to InfluxDB. Each device has a unique device id.
Metrics are written into InfluxDB in the following way. Here I only show one as an example
new Serie.Builder("perfmetric1")
.columns("time", "value", "id", "type")
.values(getTime(), getPerf1(), getId(), getType())
.build()
Writing data is fast and easy. But I saw bad performance when I run query. I'm trying to get all 15 metric values for the last one hour.
select value from perfmetric1, perfmetric2, ..., permetric15
where id='testdeviceid' and time > now() - 1h
For an hour, each metric has 120 data points, in total it's 1800 data points. The query takes about 5 seconds on a c4.4xlarge EC2 instance when it's idle.
I believe InfluxDB can do better. Is this a problem of my schema design, or is it something else? Would splitting the query into 15 parallel calls go faster?
As #valentin answer says, you need to build an index for the id column for InfluxDB to perform these queries efficiently.
In 0.8 stable you can do this "indexing" using continuous fanout queries. For example, the following continuous query will expand your perfmetric1 series into multiple series of the form perfmetric1.id:
select * from perfmetric1 into perfmetric1.[id];
Later you would do:
select value from perfmetric1.testdeviceid, perfmetric2.testdeviceid, ..., permetric15.testdeviceid where time > now() - 1h
This query will take much less time to complete since InfluxDB won't have to perform a full scan of the timeseries to get the points for each testdeviceid.
Build an index on id column. Seems that he engine uses full scan on table to retrieve data. By splitting your query in 15 threads, the engine will use 15 full scans and the performance will be much worse.