In as we speak’s world, the reliability of knowledge options is the whole lot. Once we construct dashboards and studies, one expects that the numbers mirrored there are right and up-to-date. Based mostly on these numbers, insights are drawn and actions are taken. For any unexpected cause, if the dashboards are damaged or if the numbers are incorrect — then it turns into a fire-fight to repair the whole lot. If the problems usually are not mounted in time, then it damages the belief positioned on the info staff and their options.
However why would dashboards be damaged or have unsuitable numbers? If the dashboard was constructed accurately the primary time, then 99% of the time the difficulty comes from the info that feeds the dashboards — from the info warehouse. Some potential eventualities are:
- Few ETL pipelines failed, so the brand new information isn’t but in
- A desk is changed with one other new one
- Some columns within the desk are dropped or renamed
- Schemas in information warehouse have modified
- And lots of extra.
There may be nonetheless an opportunity that the difficulty is on the Tableau website, however in my expertise, many of the occasions, it’s all the time because of some modifications in information warehouse. Although we all know the basis trigger, it’s not all the time easy to start out engaged on a repair. There may be no central place the place you may test which Tableau information sources depend on particular tables. When you have the Tableau Data Management add-on, it might assist, however from what I do know, its onerous to search out dependencies of customized sql queries utilized in information sources.
However, the add-on is simply too costly and most firms don’t have it. The actual ache begins when it’s a must to undergo all the info sources manually to start out fixing it. On high of it, you’ve gotten a string of customers in your head impatiently ready for a quick-fix. The repair itself may not be troublesome, it will simply be a time-consuming one.
What if we might anticipate these points and establish impacted information sources earlier than anybody notices an issue? Wouldn’t that simply be nice? Nicely, there’s a means now with the Tableau Metadata API. The Metadata API makes use of GraphQL, a question language for APIs that returns solely the info that you simply’re considering. For more information on what’s potential with GraphQL, do try GraphQL.org.
On this weblog publish, I’ll present you ways to hook up with the Tableau Metadata API utilizing Python’s Tableau Server Consumer (TSC) library to proactively establish information sources utilizing particular tables, to be able to act quick earlier than any points come up. As soon as you recognize which Tableau information sources are affected by a selected desk, you may make some updates your self or alert the homeowners of these information sources in regards to the upcoming modifications to allow them to be ready for it.
Connecting to the Tableau Metadata API
Lets connect with the Tableau Server utilizing TSC. We have to import in all of the libraries we would wish for the train!
### Import all required libraries
import tableauserverclient as t
import pandas as pd
import json
import ast
import reWith a purpose to connect with the Metadata API, you’ll have to first create a private entry token in your Tableau Account settings. Then replace the
### Hook up with Tableau server utilizing private entry token
tableau_auth = t.PersonalAccessTokenAuth("", "",
site_id="")
server = t.Server("https://dub01.on-line.tableau.com/", use_server_version=True)
with server.auth.sign_in(tableau_auth):
print("Related") Lets now get a listing of all information sources which can be revealed in your website. There are lots of attributes you may fetch, however for the present use case, lets maintain it easy and solely get the id, identify and proprietor contact info for each information supply. This will probably be our grasp checklist to which we are going to add in all different info.
############### Get all of the checklist of knowledge sources in your Web site
all_datasources_query = """ {
publishedDatasources {
identify
id
proprietor {
identify
electronic mail
}
}
}"""
with server.auth.sign_in(tableau_auth):
end result = server.metadata.question(
all_datasources_query
)Since I would like this weblog to be focussed on the way to proactively establish which information sources are affected by a selected desk, I’ll not be going into the nuances of Metadata API. To raised perceive how the question works, you may consult with a really detailed Tableau’s personal Metadata API documentation.
One factor to notice is that the Metadata API returns information in a JSON format. Relying on what you might be querying, you’ll find yourself with a number of nested json lists and it may possibly get very difficult to transform this right into a pandas dataframe. For the above metadata question, you’ll find yourself with a end result which would love beneath (that is mock information simply to offer you an thought of what the output seems like):
{
"information": {
"publishedDatasources": [
{
"name": "Sales Performance DataSource",
"id": "f3b1a2c4-1234-5678-9abc-1234567890ab",
"owner": {
"name": "Alice Johnson",
"email": "[email protected]"
}
},
{
"identify": "Buyer Orders DataSource",
"id": "a4d2b3c5-2345-6789-abcd-2345678901bc",
"proprietor": {
"identify": "Bob Smith",
"electronic mail": "[email protected]"
}
},
{
"identify": "Product Returns and Profitability",
"id": "c5e3d4f6-3456-789a-bcde-3456789012cd",
"proprietor": {
"identify": "Alice Johnson",
"electronic mail": "[email protected]"
}
},
{
"identify": "Buyer Segmentation Evaluation",
"id": "d6f4e5a7-4567-89ab-cdef-4567890123de",
"proprietor": {
"identify": "Charlie Lee",
"electronic mail": "[email protected]"
}
},
{
"identify": "Regional Gross sales Tendencies (Customized SQL)",
"id": "e7a5f6b8-5678-9abc-def0-5678901234ef",
"proprietor": {
"identify": "Bob Smith",
"electronic mail": "[email protected]"
}
}
]
}
}We have to convert this JSON response right into a dataframe in order that its simple to work with. Discover that we have to extract the identify and electronic mail of the proprietor from contained in the proprietor object.
### We have to convert the response into dataframe for straightforward information manipulation
col_names = end result['data']['publishedDatasources'][0].keys()
master_df = pd.DataFrame(columns=col_names)
for i in end result['data']['publishedDatasources']:
tmp_dt = {ok:v for ok,v in i.objects()}
master_df = pd.concat([master_df, pd.DataFrame.from_dict(tmp_dt, orient='index').T])
# Extract the proprietor identify and electronic mail from the proprietor object
master_df['owner_name'] = master_df['owner'].apply(lambda x: x.get('identify') if isinstance(x, dict) else None)
master_df['owner_email'] = master_df['owner'].apply(lambda x: x.get('electronic mail') if isinstance(x, dict) else None)
master_df.reset_index(inplace=True)
master_df.drop(['index','owner'], axis=1, inplace=True)
print('There are ', master_df.form[0] , ' datasources in your website')That is how the construction of master_df would seem like:
As soon as we’ve got the principle checklist prepared, we are able to go forward and begin getting the names of the tables embedded within the information sources. In case you are an avid Tableau person, you recognize that there are two methods to choosing tables in a Tableau information supply — one is to immediately select the tables and set up a relation between them and the opposite is to make use of a customized sql question with a number of tables to attain a brand new resultant desk. Subsequently, we have to deal with each the instances.
Processing of Customized SQL question tables
Under is the question to get the checklist of all customized SQLs used within the website together with their information sources. Discover that I’ve filtered the checklist to get solely first 500 customized sql queries. In case there are extra in your org, you’ll have to use an offset to get the following set of customized sql queries. There may be additionally an possibility of utilizing cursor methodology in Pagination if you wish to fetch giant checklist of outcomes (refer here). For the sake of simplicity, I simply use the offset methodology as I do know, as there are lower than 500 customized sql queries used on the positioning.
# Get the info sources and the desk names from all of the customized sql queries used in your Web site
custom_table_query = """ {
customSQLTablesConnection(first: 500){
nodes {
id
identify
downstreamDatasources {
identify
}
question
}
}
}
"""
with server.auth.sign_in(tableau_auth):
custom_table_query_result = server.metadata.question(
custom_table_query
)Based mostly on our mock information, that is how our output would seem like:
{
"information": {
"customSQLTablesConnection": {
"nodes": [
{
"id": "csql-1234",
"name": "RegionalSales_CustomSQL",
"downstreamDatasources": [
{
"name": "Regional Sales Trends (Custom SQL)"
}
],
"question": "SELECT r.region_name, SUM(s.sales_amount) AS total_sales FROM ecommerce.sales_data.Gross sales s JOIN ecommerce.sales_data.Areas r ON s.region_id = r.region_id GROUP BY r.region_name"
},
{
"id": "csql-5678",
"identify": "ProfitabilityAnalysis_CustomSQL",
"downstreamDatasources": [
{
"name": "Product Returns and Profitability"
}
],
"question": "SELECT p.product_category, SUM(s.revenue) AS total_profit FROM ecommerce.sales_data.Gross sales s JOIN ecommerce.sales_data.Merchandise p ON s.product_id = p.product_id GROUP BY p.product_category"
},
{
"id": "csql-9101",
"identify": "CustomerSegmentation_CustomSQL",
"downstreamDatasources": [
{
"name": "Customer Segmentation Analysis"
}
],
"question": "SELECT c.customer_id, c.location, COUNT(o.order_id) AS total_orders FROM ecommerce.sales_data.Prospects c JOIN ecommerce.sales_data.Orders o ON c.customer_id = o.customer_id GROUP BY c.customer_id, c.location"
},
{
"id": "csql-3141",
"identify": "CustomerOrders_CustomSQL",
"downstreamDatasources": [
{
"name": "Customer Orders DataSource"
}
],
"question": "SELECT o.order_id, o.customer_id, o.order_date, o.sales_amount FROM ecommerce.sales_data.Orders o WHERE o.order_status = 'Accomplished'"
},
{
"id": "csql-3142",
"identify": "CustomerProfiles_CustomSQL",
"downstreamDatasources": [
{
"name": "Customer Orders DataSource"
}
],
"question": "SELECT c.customer_id, c.customer_name, c.section, c.location FROM ecommerce.sales_data.Prospects c WHERE c.active_flag = 1"
},
{
"id": "csql-3143",
"identify": "CustomerReturns_CustomSQL",
"downstreamDatasources": [
{
"name": "Customer Orders DataSource"
}
],
"question": "SELECT r.return_id, r.order_id, r.return_reason FROM ecommerce.sales_data.Returns r"
}
]
}
}
}Identical to earlier than after we have been creating the grasp checklist of knowledge sources, right here additionally we’ve got nested json for the downstream information sources the place we would wish to extract solely the “identify” a part of it. Within the “question” column, your complete customized sql is dumped. If we use regex sample, we are able to simply seek for the names of the desk used within the question.
We all know that the desk names all the time come after FROM or a JOIN clause and so they typically comply with the format
### Convert the customized sql response into dataframe
col_names = custom_table_query_result['data']['customSQLTablesConnection']['nodes'][0].keys()
cs_df = pd.DataFrame(columns=col_names)
for i in custom_table_query_result['data']['customSQLTablesConnection']['nodes']:
tmp_dt = {ok:v for ok,v in i.objects()}
cs_df = pd.concat([cs_df, pd.DataFrame.from_dict(tmp_dt, orient='index').T])
# Extract the info supply identify the place the customized sql question was used
cs_df['data_source'] = cs_df.downstreamDatasources.apply(lambda x: x[0]['name'] if x and 'identify' in x[0] else None)
cs_df.reset_index(inplace=True)
cs_df.drop(['index','downstreamDatasources'], axis=1,inplace=True)
### We have to extract the desk names from the sql question. We all know the desk identify comes after FROM or JOIN clause
# Be aware that the identify of desk will be of the format ..
# Relying on the format of how desk is named, you'll have to modify the regex expression
def extract_tables(sql):
# Regex to match database.schema.desk or schema.desk, keep away from alias
sample = r'(?:FROM|JOIN)s+((?:[w+]|w+).(?:[w+]|w+)(?:.(?:[w+]|w+))?)b'
matches = re.findall(sample, sql, re.IGNORECASE)
return checklist(set(matches)) # Distinctive desk names
cs_df['customSQLTables'] = cs_df['query'].apply(extract_tables)
cs_df = cs_df[['data_source','customSQLTables']]
# We have to merge datasources as there will be a number of customized sqls utilized in the identical information supply
cs_df = cs_df.groupby('data_source', as_index=False).agg({
'customSQLTables': lambda x: checklist(set(merchandise for sublist in x for merchandise in sublist)) # Flatten & make distinctive
})
print('There are ', cs_df.form[0], 'datasources with customized sqls utilized in it') After we carry out all of the above operations, that is how the construction of cs_df would seem like:
Processing of standard Tables in Information Sources
Now we have to get the checklist of all of the common tables utilized in a datasource which aren’t part of customized SQL. There are two methods to go about it. Both use the publishedDatasources object and test for upstreamTables or use DatabaseTable and test for upstreamDatasources. I’ll go by the primary methodology as a result of I would like the outcomes at a knowledge supply stage (mainly, I would like some code able to reuse after I wish to test a selected information supply in additional element). Right here once more, for the sake of simplicity, as an alternative of going for pagination, I’m looping by means of every datasource to make sure I’ve the whole lot. We get the upstreamTables inside the sphere object in order that must be cleaned out.
############### Get the info sources with the common desk names utilized in your website
### Its greatest to extract the tables info for each information supply after which merge the outcomes.
# Since we solely get the desk info nested below fields, in case there are a whole lot of fields
# utilized in a single information supply, we are going to hit the response limits and will be unable to retrieve all the info.
data_source_list = master_df.identify.tolist()
col_names = ['name', 'id', 'extractLastUpdateTime', 'fields']
ds_df = pd.DataFrame(columns=col_names)
with server.auth.sign_in(tableau_auth):
for ds_name in data_source_list:
question = """ {
publishedDatasources (filter: { identify: """"+ ds_name + """" }) {
identify
id
extractLastUpdateTime
fields {
identify
upstreamTables {
identify
}
}
}
} """
ds_name_result = server.metadata.question(
question
)
for i in ds_name_result['data']['publishedDatasources']:
tmp_dt = {ok:v for ok,v in i.objects() if ok != 'fields'}
tmp_dt['fields'] = json.dumps(i['fields'])
ds_df = pd.concat([ds_df, pd.DataFrame.from_dict(tmp_dt, orient='index').T])
ds_df.reset_index(inplace=True)That is how the construction of ds_df would look:
We are able to have to flatten out the fields object and extract the sphere names in addition to the desk names. Because the desk names will probably be repeating a number of occasions, we must deduplicate to maintain solely the distinctive ones.
# Perform to extract the values of fields and upstream tables in json lists
def extract_values(json_list, key):
values = []
for merchandise in json_list:
values.append(merchandise[key])
return values
ds_df["fields"] = ds_df["fields"].apply(ast.literal_eval)
ds_df['field_names'] = ds_df.apply(lambda x: extract_values(x['fields'],'identify'), axis=1)
ds_df['upstreamTables'] = ds_df.apply(lambda x: extract_values(x['fields'],'upstreamTables'), axis=1)
# Perform to extract the distinctive desk names
def extract_upstreamTable_values(table_list):
values = set()a
for inner_list in table_list:
for merchandise in inner_list:
if 'identify' in merchandise:
values.add(merchandise['name'])
return checklist(values)
ds_df['upstreamTables'] = ds_df.apply(lambda x: extract_upstreamTable_values(x['upstreamTables']), axis=1)
ds_df.drop(["index","fields"], axis=1, inplace=True)As soon as we do the above operations, the ultimate construction of ds_df would look one thing like this:
We’ve all of the items and now we simply must merge them collectively:
###### Be part of all the info collectively
master_data = pd.merge(master_df, ds_df, how="left", on=["name","id"])
master_data = pd.merge(master_data, cs_df, how="left", left_on="identify", right_on="data_source")
# Save the outcomes to analyse additional
master_data.to_excel("Tableau Information Sources with Tables.xlsx", index=False)That is our last master_data:
Desk-level Impression Evaluation
Let’s say there have been some schema modifications on the “Gross sales” desk and also you wish to know which information sources will probably be impacted. Then you may merely write a small perform which checks if a desk is current in both of the 2 columns — upstreamTables or customSQLTables like beneath.
def filter_rows_with_table(df, col1, col2, target_table):
"""
Filters rows in df the place target_table is a part of any worth in both col1 or col2 (helps partial match).
Returns full rows (all columns retained).
"""
return df[
df.apply(
lambda row:
(isinstance(row[col1], checklist) and any(target_table in merchandise for merchandise in row[col1])) or
(isinstance(row[col2], checklist) and any(target_table in merchandise for merchandise in row[col2])),
axis=1
)
]
# For instance
filter_rows_with_table(master_data, 'upstreamTables', 'customSQLTables', 'Gross sales')Under is the output. You’ll be able to see that 3 information sources will probably be impacted by this alteration. It’s also possible to alert the info supply homeowners Alice and Bob upfront about this to allow them to begin engaged on a repair earlier than one thing breaks on the Tableau dashboards.
You’ll be able to try the entire model of the code in my Github repository here.
That is simply one of many potential use-cases of the Tableau Metadata API. It’s also possible to extract the sphere names utilized in customized sql queries and add to the dataset to get a field-level impression evaluation. One may also monitor the stale information sources with the extractLastUpdateTime to see if these have any points or must be archived if they don’t seem to be used any extra. We are able to additionally use the dashboards object to fetch info at a dashboard stage.
Last Ideas
When you have come this far, kudos. This is only one use case of automating Tableau information administration. It’s time to replicate by yourself work and assume which of these different duties you can automate to make your life simpler. I hope this mini-project served as an pleasant studying expertise to know the facility of Tableau Metadata API. In case you appreciated studying this, you may also like one other one in all my weblog posts about Tableau, on a few of the challenges I confronted when coping with huge .
Additionally do try my earlier weblog the place I explored constructing an interactive, database-powered app with Python, Streamlit, and SQLite.
Earlier than you go…
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