This is the official Python Client for the Keen IO API. The Keen IO API lets developers build analytics features directly into their apps.
This is still under active development. Stay tuned for improvements!
Use pip to install!
pip install keen
This client is known to work on Python 2.7, 3.5, 3.6, 3.7 and 3.8.
For versions of Python < 2.7.9, you’ll need to install pyasn1, ndg-httpsclient, pyOpenSSL.
To use this client with the Keen IO API, you have to configure your Keen IO Project ID and its access keys (if you need an account, sign up here - it's free).
Setting a write key is required for publishing events. Setting a read key is required for running queries. The recommended way to set this configuration information is via the environment. The keys you can set are KEEN_PROJECT_ID, KEEN_WRITE_KEY, KEEN_READ_KEY, and KEEN_MASTER_KEY. As per the Principle of Least Privilege, it's recommended that you not use the master_key if not necessary. This SDK will expect and use the precise key for a given operation, and throw an exception in cases of misuse.
If you don't want to use environment variables for some reason, you can directly set values as follows:
keen.project_id = "xxxx"
keen.write_key = "yyyy"
keen.read_key = "zzzz"
keen.master_key = "abcd" # not required for typical usageFor information on how to configure unique client instances, take a look at the Advanced Usage section below.
Once you've set KEEN_PROJECT_ID and KEEN_WRITE_KEY, sending events is simple:
keen.add_event("sign_ups", {
"username": "lloyd",
"referred_by": "harry"
})A data enrichment is a powerful add-on to enrich the data you're already streaming to Keen IO by pre-processing the data and adding helpful data properties. To activate add-ons, you simply add some new properties within the "keen" namespace in your events. Detailed documentation for the configuration of our add-ons is available here.
Here is an example of using the URL parser:
keen.add_event("requests", {
"page_url" : "http://my-website.com/cool/link?source=twitter&foo=bar/#title",
"keen" : {
"addons" : [
{
"name" : "keen:url_parser",
"input" : {
"url" : "page_url"
},
"output" : "parsed_page_url"
}
]
}
})Keen IO will parse the URL for you and that would equivalent to:
keen.add_event("request", {
"page_url" : "http://my-website.com/cool/link?source=twitter&foo=bar/#title",
"parsed_page_url": {
"protocol" : "http",
"domain" : "my-website.com",
"path" : "/cool/link",
"anchor" : "title",
"query_string" : {
"source" : "twitter",
"foo" : "bar"
}
}
})Here is another example of using the Datetime parser. Let's assume you want to do a deeper analysis on the "purchases" event by day of the week (Monday, Tuesday, Wednesday, etc.) and other interesting Datetime components. You can use "keen.timestamp" property that is included in your event automatically.
keen.add_event("purchases", {
"keen": {
"addons": [
{
"name": "keen:date_time_parser",
"input": {
"date_time" : "keen.timestamp"
},
"output": "timestamp_info"
}
]
},
"price": 500
})Other Data Enrichment add-ons are located in the API reference docs.
You can upload Events in a batch, like so:
# uploads 4 events total - 2 to the "sign_ups" collection and 2 to the "purchases" collection
keen.add_events({
"sign_ups": [
{ "username": "nameuser1" },
{ "username": "nameuser2" }
],
"purchases": [
{ "price": 5 },
{ "price": 6 }
]
})That's it! After running your code, check your Keen IO Project to see the event/events has been added.
Here are some examples of querying. Let's assume you've added some events to the "purchases" collection. For more code samples, take a look at Keen's docs
keen.count("purchases", timeframe="this_14_days") # => 100
keen.sum("purchases", target_property="price", timeframe="this_14_days") # => 10000
keen.minimum("purchases", target_property="price", timeframe="this_14_days") # => 20
keen.maximum("purchases", target_property="price", timeframe="this_14_days") # => 100
keen.average("purchases", target_property="price", timeframe="this_14_days") # => 49.2
keen.sum("purchases", target_property="price", group_by="item.id", timeframe="this_14_days") # => [{ "item.id": 123, "result": 240 }, { ... }]
keen.count_unique("purchases", target_property="user.id", timeframe="this_14_days") # => 3
keen.select_unique("purchases", target_property="user.email", timeframe="this_14_days") # => ["[email protected]", "[email protected]"]
# Alpha support for ordering your results and limiting what is returned is now supported in the Python SDK.
# Keep in mind that even if you limit your results with the "limit" keyword, you are still querying over the
# normal amount of data, and thus your compute costs will not change. Limit only changes what is displayed.
# The keyword "limit" must be a positive integer. The keyword "order_by" must be a dictionary with a required
# "property_name" specified and optionally a "direction". The "property_name" must be one of the "group_by"
# properties or "result". The "direction" may be either keen.direction.DESCENDING or keen.direction.ASCENDING.
# Ascending is the default direction used if no "direction" is supplied. No other keywords may be used in the
# "order_by" dictionary.
# You may only use "order_by" if you supply a "group_by". You may only use "limit" if you supply an "order_by".
# This will run a count query with results grouped by zip code.
# It will display only the top ten zip code results based upon how many times users in those zip codes logged in.
keen.count("purchases", group_by="zip_code", timeframe="this_14_days", limit=10,
order_by={"property_name": "result", "direction": keen.direction.DESCENDING})
keen.extraction("purchases", timeframe="today") # => [{ "price" => 20, ... }, { ... }]
keen.multi_analysis(
"purchases",
analyses={
"total":{
"analysis_type": "sum",
"target_property": "price"
},
"average":{
"analysis_type": "average",
"target_property": "price"
}
},
timeframe='this_14_days'
) # => {"total":10329.03, "average":933.93}
step1 = {
"event_collection": "sign_ups",
"actor_property": "user.email"
}
step2 = {
"event_collection": "purchases",
"actor_property": "user.email"
}
keen.funnel([step1, step2], timeframe="today") # => [2039, 201]To return the full API response from a funnel analysis (as opposed to the singular "result" key), set all_keys=True.
For example, keen.funnel([step1, step2], timeframe="today", all_keys=True) would return "result", "actors" and "steps" keys.
The Keen IO API allows you to delete events from event collections, optionally supplying filters, timeframe or timezone to narrow the scope of what you would like to delete.
You'll need to set your master_key.
keen.delete_events("event_collection", filters=[{"property_name": 'username', "operator": 'eq', "property_value": 'Bob'}])See below for more options.
When you upload events in a batch, some of them may succeed and some of them may have errors. The Keen API returns information on each. Here's an example:
Upload code (remember, Keen IO doesn't allow periods in property names):
response = keen.add_events({
"sign_ups": [
{ "username": "nameuser1" },
{ "username": "nameuser2", "an.invalid.property.name": 1 }
],
"purchases": [
{ "price": 5 },
{ "price": 6 }
]
})That code would result in the following API JSON response:
{
"sign_ups": [
{"success": true},
{"success": false, "error": {"name": "some_error_name", "description": "some longer description"}}
],
"purchases": [
{"success": true},
{"success": true}
]
}So in python, to check on the results of your batch, you'd have code like so:
batch = {
"sign_ups": [
{ "username": "nameuser1" },
{ "username": "nameuser2", "an.invalid.property.name": 1 }
],
"purchases": [
{ "price": 5 },
{ "price": 6 }
]
}
response = keen.add_events(batch)
for collection in response:
collection_result = response[collection]
event_count = 0
for individual_result in collection_result:
if not individual_result["success"]:
print("Event had error! Collection: '{}'. Event body: '{}'.".format(collection, batch[collection][event_count]))
event_count += 1If you intend to send events or query from different projects within the same python file, you'll need to set up unique client instances (one per project). You can do this by assigning an instance of KeenClient to a variable like so:
from keen.client import KeenClient
client = KeenClient(
project_id="xxxx", # your project ID for collecting cycling data
write_key="yyyy",
read_key="zzzz",
master_key="abcd" # not required for typical usage
)
client_hike = KeenClient(
project_id="xxxx", # your project ID for collecting hiking data (different from the one above)
write_key="yyyy",
read_key="zzzz",
master_key="abcd" # not required for typical usage
)You can send events like this:
# add an event to an event collection in your cycling project
client.add_event(...)
# or add an event to an event collection in your hiking project
client_hike.add_event(...)Similarly, you can query events like this:
client.count(...)You can manage your saved queries from the Keen python client.
# Create your KeenClient
from keen.client import KeenClient
client = KeenClient(
project_id="xxxx", # your project ID
read_key="zzzz",
master_key="abcd" # Most Saved Query functionality requires master_key
)
# Create a saved query
saved_query_attributes = {
# NOTE : For now, refresh_rate must explicitly be set to 0 unless you
# intend to create a Cached Query.
"refresh_rate": 0,
"query": {
"analysis_type": "count",
"event_collection": "purchases",
"timeframe": "this_2_weeks",
"filters": [{
"property_name": "price",
"operator": "gte",
"property_value": 1.00
}]
}
}
client.saved_queries.create("saved-query-name", saved_query_attributes)
# Get all saved queries
client.saved_queries.all()
# Get one saved query
client.saved_queries.get("saved-query-name")
# Get saved query with results
client.saved_queries.results("saved-query-name")
# NOTE : Updating Saved Queries requires sending the entire query definition. Any attribute not
# sent is interpreted as being cleared/removed. This means that properties set via another
# client, including the Projects Explorer Web UI, will be lost this way.
#
# The update() function makes this easier by allowing client code to just specify the
# properties that need updating. To do this, it will retrieve the existing query definition
# first, which means there will be two HTTP requests. Use update_full() in code that already
# has a full query definition that can reasonably be expected to be current.
# Update a saved query to now be a cached query with the minimum refresh rate of 4 hrs...
# ...using partial update:
client.saved_queries.update("saved-query-name", { "refresh_rate": 14400 })
# ...using full update, if we've already fetched the query definition:
saved_query_attributes["refresh_rate"] = 14400
client.saved_queries.update_full("saved-query-name", saved_query_attributes)
# Update a saved query to a new resource name...
# ...using partial update:
client.saved_queries.update("saved-query-name", { "query_name": "cached-query-name" })
# ...using full update, if we've already fetched the query definition or have it lying around
# for whatever reason. We send "refresh_rate" again, along with the entire definition, or else
# it would be reset:
saved_query_attributes["query_name"] = "cached-query-name"
client.saved_queries.update_full("saved-query-name", saved_query_attributes)
# Delete a saved query (use the new resource name since we just changed it)
client.saved_queries.delete("cached-query-name")# Create your KeenClient
from keen.client import KeenClient
client = KeenClient(
project_id="xxxx", # your project ID
read_key="zzzz",
master_key="abcd"
)
# Create a Cached Dataset
dataset_name = "NEW_DATASET"
display_name = "My new dataset"
query = {
"project_id": "PROJECT ID",
"analysis_type": "count",
"event_collection": "purchases",
"filters": [
{
"property_name": "price",
"operator": "gte",
"property_value": 100
}
],
"timeframe": "this_500_days",
"timezone": None,
"interval": "daily",
"group_by": ["ip_geo_info.country"]
}
index_by = "product.id"
client.cached_datasets.create(
dataset_name,
query,
index_by,
display_name
)
# Get all Cached Datasets
client.cached_datasets.all()
# Get one Cached Dataset
client.cached_datasets.get(dataset_name)
# Retrieve Cached Dataset results
index_by = "a_project_id"
timeframe ="this_2_hours"
client.cached_datasets.results(
dataset_name,
index_by,
timeframe
)
# Using an absolute timeframe
timeframe = {
"start": "2018-11-02T00:00:00.000Z",
"end": "2018-11-02T02:00:00.000Z"
}
client.cached_datasets.results(
dataset_name,
index_by,
timeframe
)
# Using multiple index_by values
index_by = {
"project.id": "a_project_id",
"project.foo": "bar"
}
client.cached_datasets.results(
"dataset_with_multiple_indexes",
index_by,
timeframe
)
# Delete a Cached Dataset
client.cached_datasets.delete(dataset_name)Two time-related properties are included in your event automatically. The properties “keen.timestamp” and “keen.created_at” are set at the time your event is recorded. You have the ability to overwrite the keen.timestamp property. This could be useful, for example, if you are backfilling historical data. Be sure to use ISO-8601 Format.
Keen stores all date and time information in UTC!
keen.add_event("sign_ups", {
"keen": {
"timestamp": "2012-07-06T02:09:10.141Z"
},
"username": "lloyd",
"referred_by": "harry"
})By default, GET requests will timeout after 305 seconds. If you want to manually override this, you can create a KeenClient with the "get_timeout" parameter. This client will fail GETs if no bytes have been returned by the server in the specified time. For example:
from keen.client import KeenClient
client = KeenClient(
project_id="xxxx",
write_key="yyyy",
read_key="zzzz",
get_timeout=100
)This will cause queries such as count(), sum(), and average() to timeout after 100 seconds. If this timeout limit is hit, a requests.Timeout will be raised. Due to a bug in the requests library, you might also see an SSLError (#1294)
By default, POST requests will timeout after 305 seconds. If you want to manually override this, you can create a KeenClient with the "post_timeout" parameter. This client will fail POSTs if no bytes have been returned by the server in the specified time. For example:
from keen.client import KeenClient
client = KeenClient(
project_id="xxxx",
write_key="yyyy",
post_timeout=100
)This will cause both add_event() and add_events() to timeout after 100 seconds. If this timeout limit is hit, a requests.Timeout will be raised. Due to a bug in the requests library, you might also see an SSLError (https://github.com/kennethreitz/requests/issues/1294)
The Python client enables the creation and manipulation of Access Keys. Examples:
from keen.client import KeenClient
# You could also simply use: import keen
# If you do this, you will need your project ID and master key set in environment variables.
client = KeenClient(
project_id="xxxx",
master_key="zzzz"
)
# Create an access key. See: https://keen.io/docs/access/access-keys/#customizing-your-access-key
client.create_access_key(name="Dave_Barry_Key", is_active=True, permitted=["writes", "cached_queries"],
options={"cached_queries": {"allowed": ["dave_barry_in_cyberspace_sales"]}})
# Display all access keys associated with this client's project.
client.list_access_keys()
# Get details on a particular access key.
client.get_access_key(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Revoke (disable) an access key.
client.revoke_access_key(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Unrevoke (re-enable) an access key.
client.unrevoke_access_key(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Delete an access key
client.delete_access_key(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Change just the name of an access key.
client.update_access_key_name(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", name="Some_New_Name")
# Add new access key permissions to existing permissions on a given key.
# In this case the set of permissions currently contains "writes" and "cached_queries".
# This function call keeps the old permissions and adds "queries" to that set.
# ("writes", "cached_queries") + ("queries") = ("writes", "cached_queries", "queries")
client.add_access_key_permissions(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", permissions=["queries"])
# Remove one or more access key permissions from a given key.
# In this case the set of permissions currently contains "writes", "cached_queries", and "queries".
# This function call will keep the old permissions not explicitly removed here.
# So we will remove both "writes" and "queries" from the set, leaving only "cached_queries".
# ("writes", "cached_queries", "queries") - ("writes", "queries") = ("cached_queries")
client.remove_access_key_permissions(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", permissions=["writes", "queries"])
# We can also perform a full update on the permissions, replacing all existing permissions with a new list.
# In this case our existing permissions contains only "cached_queries".
# We will replace this set with the "writes" permission with this function call.
# ("cached_queries") REPLACE-WITH ("writes") = ("writes")
client.update_access_key_permissions(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", permissions=["writes"])
# Replace all existing key options with this new options object.
client.update_access_key_options(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", options={"writes": {
"autofill": {
"customer": {
"id": "93iskds39kd93id",
"name": "Ada Corp."
}
}
}})
# Replace everything but the key ID with what is supplied here.
# If a field is not supplied here, it will be set to a blank value.
# In this case, no options are supplied, so all options will be removed.
client.update_access_key_full(key="ABCDEFGHIJKLMNOPQRSTUVWXYZ", name="Strong_Bad", is_active=True, permitted=["queries"])The Python client enables you to create Scoped Keys easily, but Access Keys are better! If you need to use them anyway, for legacy reasons, here's how:
from keen.client import KeenClient
from keen import scoped_keys
api_key = KEEN_MASTER_KEY
write_key = scoped_keys.encrypt(api_key, {"allowed_operations": ["write"]})
read_key = scoped_keys.encrypt(api_key, {"allowed_operations": ["read"]})write_key and read_key now contain scoped keys based on your master API key.
To run tests:
python setup.py test
This project is in alpha stage at version 0.7.0. See the full CHANGELOG.
If you have any questions, bugs, or suggestions, please report them via Github Issues. We'd love to hear your feedback and ideas!
This is an open source project and we love involvement from the community! Hit us up with pull requests and issues.