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Elasticsearch prevent indexing of Markdown hyperlinks

I am building a Markdown file content search using Elasticsearch. Currently the whole content inside the MD file is indexed in Elasticsearch. But the problem is it shows results like this [Mylink](https://link-url-here.org), [Mylink2](another_page.md)
in the search results.
I would like to prevent indexing of hyperlinks and reference to other pages. When someone search for "Mylink" it should only return the text without the URL. It would be great if someone could help me with the right solution for this.

You need to render Markdown in your indexing application, then remove HTML tags and save it alongside with the markdown source.

I think you have two main solutions for this problem.
first: clean the data in your source code before indexing it into Elasticsearch.
second: use the Elasticsearch filter to clean the data for you.
the first solution is the easy one but if you need to do this process inside the Elasticsearch you need to create a ingest pipeline.
then you can use the Script processor to clean the data you need by a ruby script that can find your regex and remove it

You could use an ingest pipeline with a script processor to extract the link text:
1. Set up the pipeline
PUT _ingest/pipeline/clean_links
{
"description": "...",
"processors": [
{
"script": {
"source": """
if (ctx["content"] == null) {
// nothing to do here
return
}
def content = ctx["content"];
Pattern pattern = /\[([^\]\[]+)\](\(((?:[^\()]+)+)\))/;
Matcher matcher = pattern.matcher(content);
def purged_content = matcher.replaceAll("$1");
ctx["purged_content"] = purged_content;
"""
}
}
]
}
The regex can be tested here and is inspired by this.
2. Include the pipeline when ingesting the docs
POST my-index/_doc?pipeline=clean_links
{
"content": "[Mylink](https://link-url-here.org) [anotherLink](http://dot.com)"
}
POST my-index/_doc?pipeline=clean_links
{
"content": "[Mylink2](another_page.md)"
}
The python docs are here.
3. Verify
GET my-index/_search?filter_path=hits.hits._source
should yield
{
"hits" : {
"hits" : [
{
"_source" : {
"purged_content" : "Mylink anotherLink",
"content" : "[Mylink](https://link-url-here.org) [anotherLink](http://dot.com)"
}
},
{
"_source" : {
"purged_content" : "Mylink2",
"content" : "[Mylink2](another_page.md)"
}
}
]
}
}
You could instead replace the original content if you want to fully discard them from your _source.
In contrast, you could go a step further in the other direction and store the text + link pairs in a nested field of the form:
{
"content": "...",
"links": [
{
"text": "Mylink",
"href": "https://link-url-here.org"
},
...
]
}
so that when you later decide to make them searchable, you'll be able to do so with precision.
Shameless plug: you can find other hands-on ingestion guides in my Elasticsearch Handbook.

singer tap-zendesk - how to extract catalog.json with selected:True from discovery mode

I am using singer's tap-zendesk library and want to extract data from specific schemas.
I am running the following command in sync mode:
tap-zendesk --config config.json --catalog catalog.json.
Currently my config.json file has the following parameters:
{
"email": "<email>",
"api_token": "<token>",
"subdomain": "<domain>",
"start_date": "<start_date>"
}
I've managed to extract data by putting 'selected':true under schema, properties and metadata in the catalog.json file. But I was wondering if there was an easier way to do this? There are around 15 streams I need to go through.
I manage to get the catalog.json file through the discovery mode command:
tap-zendesk --config config.json --discover > catalog.json
The output looks something like the following, but that means that I have to go and add selected:True under every field.
{
"streams": [
{
"stream": "tickets",
"tap_stream_id": "tickets",
"schema": {
**"selected": "true"**,
"properties": {
"organization_id": {
**"selected": "true"**,},
"metadata": [
{
"breadcrumb": [],
"metadata": {
**"selected": "true"**
}

The selected=true needs to be applied only once per stream. This needs to be added to the metadata section under the stream where breadcrumbs = []. This is very poorly documented.
Please see this blog post for some helpful details: https://medium.com/getting-started-guides/extracting-ticket-data-from-zendesk-using-singer-io-tap-zendesk-57a8da8c3477

How can I access objects of an array in a query?

I am currently working on a project for my school. The goal is to analyze logsfiles that are generated by salt-stack. I have already setup a MongoDB and the MONGO_FUTURE_RETURN Returner in salt-stack.
I want to analyze the logfiles via a python script. Eveything is connected and basic queries such as db.saltReturns.find() work fine.
Since I do not want to rewrite the retuner and my programm needs to access different objects I need something like "Object.Everything.nestedObject".
To clarify what I mean I have attached a snippet of data and will show you what I would want to access.
I have already tried to use $, $each and $[] - none of them solved my problem.
"fun_args" : [ ],
"jid" : "20190423135733454092",
"return" : {
"cmd_|-sssd-ldap-cmd-pam-auth-update-bugfix_|-/usr/local/bin/bugfix-682662-sh_|-wait" : {
"comment" : "No changes detected",
"start_time" : "13:58:22.852410",
"result" : true,
"duration" : 0.016,
"__run_num__" : 26,
"__sls__" : "sssd-ldap.install",
"changes" : {
}
},
"pkg_|-salt-minion-required-packages_|-salt-minion-required-packages_|-installed" : {
"comment" : "All specified packages are already installed",
"name" : "python-concurrent.futures",
"start_time" : "13:58:18.915102",
"result" : true,
"duration" : 24.703,
"__run_num__" : 5,
"__sls__" : "salt-minion.install",
"changes" : {
},
"__id__" : "salt-minion-required-packages"
}
...
}
In my script I would like to access:
"full_ret.return.[all].comment"
All I need is a positional operator that can replace the [all] placeholder.

I have searched many forums and documentations but could not find a wildcard operator for mongoDB queries. Sadly this seems to be a feature that will not be implemented.

Access GlobalParameters in Azure ML Python script

How can one access the global parameters ("GlobalParameters") sent from a web service in a Python script on Azure ML?
I tried:
if 'GlobalParameters' in globals():
myparam = GlobalParameters['myparam']
but with no success.
EDIT: Example
In my case, I'm sending a sound file over the web service (as a list of samples). I would also like to send a sample rate and the number of bits per sample. I've successfully configured the web service (I think) to take these parameters, so the GlobalParameters now look like:
"GlobalParameters": {
"sampleRate": "44100",
"bitsPerSample": "16",
}
However, I cannot access these variables from the Python script, neither as GlobalParameters["sampleRate"] nor as sampleRate. Is it possible? Where are they stored?

based on our understanding of your question, here may has a miss conception that Azure ML parameters are not “Global Parameters”, as a matter of fact they are just parameter substitution tied to a particular module. So in affect there are no global parameters that are accessible throughout the experiment you have mentioned. Such being the case, we think the experiment below accomplishes what you are asking for:
Please add an “Enter Data” module to the experiment and add Data in csv format. Then for the Data click the parameter to create a web service parameter. Add in the CSV data which will be substituted from data passed by the client application. I.e.
Please add an “Execute Python” module and hook up the “Enter Data” output to the “Execute Python” input1. Add the python code to take the dataframe1 and add it to a python list. Once you have it in a list you can use it anywhere in your python code.
Python code snippet
def azureml_main(dataframe1 = None, dataframe2 = None):
import pandas as pd
global_list = []
for g in dataframe1["Col3"]:
global_list.append(g)
df_global = pd.DataFrame(global_list)
print('Input pandas.DataFrame:\r\n\r\n{0}'.format(df_global))
return [df_global]
Once you publish your experiment, you can add in new values in the “Data”: “”, section below with the new values that you was substituted for the “Enter Data” values in the experiment.
data = {
"Inputs": {
"input1":
{
"ColumnNames": ["Col1", "Col2", "Col3"],
"Values": [ [ "0", "value", "0" ], [ "0", "value", "0" ], ]
}, },
"GlobalParameters": {
"Data": "1,sampleRate,44500\\n2,bitsPerSample,20",
}
}
Please feel free to let us know if this makes sense.

The GlobalParameters parameter can not be used in a Python script. It is used to override certain parameters in other modules.
If you, for example, take the 'Split Data' module, you'll find an option to turn a parameter into a web service parameter:
Once you click that, a new section appears titled "Web Service Parameters". There you can change the default parameter name to one of your choosing.
If you deploy your project as a web service, you can override that parameter by putting it in the GlobalParameters parameter:
"GlobalParameters": {
"myFraction": 0.7
}
I hope that clears things up a bit.

Although it is not possible to use GlobalParameters in the Python script (see my previous answer), you can however hack/abuse the second input of the Python script to pass in other parameters. In my example I call them metadata parameters.
To start, I added:
a Web service input module with name: "realdata" (for your real data off course)
a Web service input module with name: "metadata" (we will abuse this one to pass parameters to our Python).
a Web service output module with name: "computedMetadata"
Connect the modules as follows:
As you can see, I also added a real data set (Restaurant ratings) as wel as a dummy metadata csv (the Enter Data Manually) module.
In this manual data you will have to predefine your metadata parameters as if they were a csv with a header and a only a single row to hold the data:
In the example both sampleRate and bitsPerSample are set to 0.
My Python scripts then takes in that fake csv as metadata, does some dummy calculation with it and returns it as column name:
import pandas as pd
def azureml_main(realdata = None, metadata = None):
theSum = metadata["sampleRate"][0] + metadata["bitsPerSample"][0]
outputString = "The sum of the sampleRate and the bitsPerSecond is " + str(theSum)
print(outputString)
return pd.DataFrame([outputString])
I then published this as a web service and called it using Node.js like this:
httpreq.post('https://ussouthcentral.services.azureml.net/workspaces/xxx/services/xxx', {
headers: {
Authorization: 'Bearer xxx'
},
json: {
"Inputs": {
"realdata": {
"ColumnNames": [
"userID",
"placeID",
"rating"
],
"Values": [
[
"100",
"101",
"102"
],
[
"200",
"201",
"202"
]
]
},
"metadata": {
"ColumnNames": [
"sampleRate",
"bitsPerSample"
],
"Values": [
[
44100,
16
]
]
}
},
"GlobalParameters": {}
}
}, (err, res) => {
if(err) return console.log(err);
console.log(JSON.parse(res.body));
});
The output was as expected:
{ Results:
{ computedMetadata:
{ type: 'table',
value:
{ ColumnNames: [ '0' ],
ColumnTypes: [ 'String' ],
Values:
[ [ 'The sum of the sampleRate and the bitsPerSecond is 44116' ] ] } } } }
Good luck!

Getting full list of revisions on document level using CouchDB-Python?

I was working couchdb-python ( http://code.google.com/p/couchdb-python/ ) and i was wondering if I have any way to retrieve a full list
of revisions that have occurred on a document level?
Suppose I have a database named "movies" and it contains several documents.
Each of my documents have more than 3 revisions.
Can I retrieve my documents based on the revisions?
If yes, how? I didn't see any obvious method to do it using CouchDB-Python

I am not sure about couchdb-python, however you can get the entire known revision history of a document via the HTTP API.
Learn all about it in the CouchDB Document API documentation.
A normal query:
$ curl jhs.couchone.com/db/doc
{ _id: 'doc',
_rev: '3-825cb35de44c433bfb2df415563a19de' }
Add ?revs=true to see an array of old revisions.
$ curl jhs.couchone.com/db/doc?revs=true
{ _id: 'doc',
_rev: '3-825cb35de44c433bfb2df415563a19de',
_revisions:
{ start: 3,
ids:
[ '825cb35de44c433bfb2df415563a19de',
'7051cbe5c8faecd085a3fa619e6e6337',
'967a00dff5e02add41819138abb3284d' ] } }
Also you can add ?revs_info=true for more details about the revisions, such as whether they are still available (i.e. they were added after the last compaction and you can fetch them).
$ curl jhs.couchone.com/db/doc?revs_info=true
{ _id: 'doc',
_rev: '3-825cb35de44c433bfb2df415563a19de',
_revs_info:
[ { rev: '3-825cb35de44c433bfb2df415563a19de',
status: 'available' },
{ rev: '2-7051cbe5c8faecd085a3fa619e6e6337',
status: 'available' },
{ rev: '1-967a00dff5e02add41819138abb3284d',
status: 'available' } ] }

The Database.revisions method may be what you want, http://code.google.com/p/couchdb-python/source/browse/couchdb/client.py#545.