Say you have an average looking database. And you want to generate a variety of text files (each with their own specific formatting - so the files may have rudimentary tables and spacing). So you'd be taking the data from the Database, transforming it in a specified format (while doing some basic logic) and saving it as a text file (you can store it in XML as an intermediary step).
So if you had to create 10 of these unique files what would be the ideal approach to creating these files? I suppose you can create classes for each type of transformation but then you'd need to create quite a few classes, and what if you needed to create another 10 more of these files (a year down the road)?
What do you think is a good approach to this problem? being able to maintain the customizability of the output file, yet not creating a mess of a code and maintenance effort?
Here is what I would do if I were to come up with a general approach to this vague question. I would write three pieces of code, independent of each other:
a) A query processor which can run a query on a given database and output results in a well-known xml format.
b) An XSL stylesheet which can interpret the well-known xml format in (a) and transform it to the desired format.
c) An XML-to-Text transformer which can read the files in (a) and (b) and put out the result.
Related
For example
Instead of a field named, COLUMN_1, that holds an embedded document with fields A, B, and C.
Why not 3 separate fields with names COLUMN_1_A, COLUMN_1_B, COLUMN_1_C.
In mongoDB you prefer to work with documents the way they exist naturaly
, afcourse you can create routines that can translate every time and assemble/disasemble the document:
COLUMN_1:{A:X,B:Y,C:Z}
to:
COLUMN_1_A:X,COLUMN_1_B:Y,COLUMN_1_C:Z
But this is additonal work that you dont want to do every time , you are a lazy effective developer that prefer just store your json document the way it will be used unless there is a specific use case that make sence to do it this way ... :)
Also please, note embedding is possible up to 100 levels , but max document size is still limited to 16MB , so in your document model it is not expected that you embed all your database in single document...
When you need performance , you add indices and optimize queries , that way your most searched data stay in memory , there is no big difference if your fields are embeded or in the root if they are not indexed ...
I'm developing a new webpage in (.NET framework, if that helps) for the below scenario. Every single day, we get a cab drivers report.
Date | Blob
-------------------------------------------------------------
15/07 | {"DriverName1":"100kms", "DriverName2":"10kms", "Hash":"Value"...}
16/07 | {"DriverName1":"50kms", "DriverName3":"100kms", "Hash":"Value"}
Notice that the 'Blob' is the actual data received in json format - contains information about the distance covered by a driver at that particular day.
I have written a service which reads the above table & further breaks down this and puts it into a new table like below:
Date | DriverName | KmsDriven
15/07 DriverName1 100
15/07 DriverName2 10
16/07 DriverName3 100
16/07 DriverName1 50
By populating this, I can easily do the following queries:
How many drivers drove on that particular day.
How is 'DriverName1' did for that particular week, etc.,
My questions here are:
Are there anything in .NET / SQL world to specifically address this or let me know if I am reinventing the wheel here.
Is this the right way to use the Blob data ?
Are there any design patterns to adhere here to ?
Are there anything in .NET / SQL world to specifically address this or
let me know if I am reinventing the wheel here.
Well, there are JSON parsers available, for example Newtonsoft's Json.NET. Or you can use SQL Server's own functions. Once you have extracted individual values from JSON, you can write them into corresponding columns (in your new table).
Is this the right way to use the Blob data?
No. It violates the principle of atomicity, and therefore the first normal form.
Are there any design patterns to adhere here to?
I'm not sure about "patterns", but I don't see why would you need a BLOB in this case.
Assuming the data is uniform (i.e. it always has the same fields), you can just declare the columns you need and write directly to them (as you already proposed).
Otherwise, you may consider using SQL Server's XML data type, which will enable you to extract some of the sections within an XML document, or insert a new section without replacing your whole document.
My problem is as follows: Let's say I have three files. A, B, and C. Each of these files contains 100-150M strings (one per line). Each string is in the format of a hierarchical path like /e/d/f. For example:
File A (RTL):
/arbiter/par0/unit1/sigA
/arbiter/par0/unit1/sigB
...
/arbiter/par0/unit2/sigA
File B (SCH)
/arbiter_sch/par0/unit1/sigA
/arbiter_sch/par0/unit1/sigB
...
/arbiter_sch/par0/unit2/sigA
File C (Layout)
/top/arbiter/par0/unit1/sigA
/top/arbiter/par0/unit1/sigB
...
/top/arbiter/par0/unit2/sigA
We can think of file A corresponding to circuit signals in a hardware modeling language. File B corresponding to circuit signals in a schematic netlist. File C corresponding to circuit signals in a layout (for manufacturing).
Now a signal will have a mapping between File A <-> File B <-> File C. For example in this case, /arbiter/par0/unit1/sigA == /arbiter_sch/par0/unit1/sigA == /top/arbiter/par0/unit1/sigA. Of course, this association (equivalence) is established by me, and I don't expect the matcher to figure this out for me.
Now say, I give '/arbiter/par0/unit1/sigA'. In this case, the matcher should return a direct match from file A since it is found. For file B/C a direct match is not possible. So it should return the best possible matches (i.e., edit distance?) So in this example, it can give /arbiter_sch/par0/unit1/sigA from file B and /top/arbiter/par0/unit1/sigA from file C.
Instead of giving a full string search, I could also give something like *par0*unit1*sigA and it should give me all the possible matches from fileA/B/C.
I am looking for solutions, and came across Apache Lucene. However, I am not totally sure if this would work. I am going through the docs to get some idea.
My main requirements are the following:
There will be 3 text files with full path to signals. (I can adjust the format to make it more compact if it helps building the indexer more quickly).
Building the index should be fairly fast (take a couple of hours). The files above are static (no modifications).
Searching should be comprehensive. It is OK if it takes ~1s / search but the matching should support direct match, regex match, and edit distance matching. The main challenge is each file can have 100-150 million signals.
Can someone tell me if such a use case can be easily addressed by Lucene? What would be the correct way to go about building a index and doing quick/fast searching? I would like to write some proof-of-concept code and test the performance. Thanks.
i think based on your requirements the best solution would be a PoC with a given test set of entries. Based on this it should be possible to evaluate the target indexing time you like to achieve. Because you only use static informations it's easier, because do don't have to care about topics like NRT (near-real-time searches).
Personally i never used lucene for such a big information set but i think lucene is able to handle this.
How i would do it:
Read tutorials and best practices about lucene, indexing, searching and understand how it works
Define an data set for indexing lets say 1000 lines for each file
Define your lucene document structure
this is really important because based on this you will apply your
searches. take care about analyzer tasks like tokanization if needed
and how. If you need fulltext search care about a TextField.
Write code for simple indexing
Run small tests with indexing and inspect your index with Luke
Write code for simple searching
Define queries and your expected results. execute searches and check
results.
Try to structure your code. separate indexing and searching -> it will be easier to refactor.
I have a ETL which give text file output and I have to check the those text content has the word error or bad using pentaho.
Is there any simple way to find it?
If you are trying to process a number of files, you can use a Get Filenames step to get all the filenames. Then, if your text files are small, you can use a Get File Content step to get the whole file as one row, then use a Java Filter or other matching step (RegEx, e.g.) to search for the words.
If your text files are too big but line-based or otherwise in a fixed format (which it likely is if you used a text file output step), you can use a Text File Input step to get the lines, then a matcher step (see above) to find the words in the line. Then you can use a Filter Rows step to choose just those rows that contain the words, then Select Values to choose just the filename, then a Sort Rows on the filename, then a Unique Rows step. The result should be a list of filenames whose contents contain the search words.
This may seem like a lot of steps, but Pentaho Data Integration or PDI (aka Kettle) is designed to be a flow of steps with distinct (and very reusable) functionality. A smaller but less "PDI" method is to write a User Defined Java Class (or other scripting) step to do all the work. This solution has a smaller number of steps but is not very configurable or reusable.
If you're writing these files out yourself, then dont you already know the content? So scan the fields at the point at which you already have them in memory.
If you're trying to see if Pentaho has written an error to the file, then you should use error handling on the output step.
Finally PDI is not a text searching tool. If you really need to do this, then probably best bet is good old grep..
Does anyone have any experience adding their own data to GapMinder, the really cool software that Hans Rosling uses in his TED talks? I have an array od objects in JSON that would be easy to show in moving bubbles. This would be really cool.
I can see that my Ubuntu box has what looks like data in /opt/Gapminder Desktop/share/assets/graphs/world, but I would need to figure out:
How to add a measure to a graph
How to add a data series
How to set the time range of the data
Identify the measures to follow at each time step
and so on.
Just for the record: if you want to use Gapminder with your own dataset, you have to convert your data in a format suitable to Gapminder. More specifically, looking in the assets/graphs/world, you will have to:
Edit the file overview.xml, which contains the tree structure of all the indicators (just copy/paste an entry and specify your own data);
Convert your data copying the structure of the xml files in that directory (this is the tricky part): you can specify some metadata in the preamble, and then specify your own data series, with something like:
<t1 m="i20,50.0,99.0,1992" d="90.0, ... ,50.0, ..."/> where i20 is the country id, which is followed by the minima and maxima of the series, and the year it refers to.
In my humble opinion, Gapminder is a great app but it definitely needs more work on integration with other datasets. Way better to use Google Motion Chart as you did, or MooGraph (site and doc), which is unfortunately not as great as Gapminder.
#Stefano
the information you provided is very valuable. Is somewhere available a detailed specification of the XML files containing the data?
Anyway, just to enrich your response, I also found that:
overview.xml file
The link between Nations and their IDs is in this file
The structure of the menus for the selection of the indicators is also in the same file (at the bottom) under the section <indicatorCategorization>
The structure of the datafile XML
For each line the year represents the first year of the serie, and then the values follow one per year, comma separated.
Grazie,
Max
I ended up using the google motion chart API. I ended up with this.