I am new to Salesforce Marketing Cloud and journey builder.
https://developer.salesforce.com/docs/marketing/marketing-cloud/guide/creating-activities.html
We are building journey builder's custom activity in which it will use a data extension as the source and when the journey builder is invoked, it will fetch a row and send this data to our company's internal endpoint. The team got that part working. We are using the postmonger.js.
I have a couple of questions:
Is there a way to retrieve the data from data extension in bulk so that we can call our company's internal bulk endpoint? Calling the endpoint for each record in the data extension for our use case would not be efficient enough and won't work.
When the journey is invoked and an entry in the data extension is retrieved and that data is sent to our internal endpoint, is there a machanism to mark this entry as already sent such that next time the journey is run, it won't process the entry that's already sent?
Here is a snippet of our customActivity.js in which this is populating one record. (I changed some variable names.). Is there a way to populate multiple records such that when "execute" is called, it is passing a list of payloads as input to our internal endpoint.
function save() {
try {
var TemplateNameValue = $('#TemplateName').val();
var TemplateIDValue = $('#TemplateID').val();
let auth = "{{Contact.Attribute.Authorization.Value}}"
payload['arguments'].execute.inArguments = [{
"vendorTemplateId": TemplateIDValue,
"field1": "{{Contact.Attribute.DD.field1}}",
"eventType": TemplateNameValue,
"field2": "{{Contact.Attribute.DD.field2}}",
"field3": "{{Contact.Attribute.DD.field3}}",
"field4": "{{Contact.Attribute.DD.field4}}",
"field5": "{{Contact.Attribute.DD.field5}}",
"field6": "{{Contact.Attribute.DD.field6}}",
"field7": "{{Contact.Attribute.DD.field7}}",
"messageMetadata" : {}
}];
payload['arguments'].execute.headers = `{"Authorization":"${auth}"}`;
payload['configurationArguments'].stop.headers = `{"Authorization":"default"}`;
payload['configurationArguments'].validate.headers = `{"Authorization":"default"}`;
payload['configurationArguments'].publish.headers = `{"Authorization":"default"}`;
payload['configurationArguments'].save.headers = `{"Authorization":"default"}`;
payload['metaData'].isConfigured = true;
console.log(payload);
connection.trigger('updateActivity', payload);
} catch(err) {
document.getElementById("error").style.display = "block";
document.getElementById("error").innerHtml = err;
}
console.log("Template Name: " + JSON.stringify(TemplateNameValue));
console.log("Template ID: " + JSON.stringify(TemplateIDValue));
}
});
Any advise or idea is highly appreciated!
Thank you.
Grace
Firstly, i implore you to not proceed with the design pattern of fetching data for each subscriber, from Marketing Cloud, that gets sent through the custom activity, for arguments sake i'll list two big issues.
You have no way of limiting the configuration of data extensions columns or column names in SFMC (Salesforce Marketing Cloud). If any malicious user or by human error would delete a column or change a column name your service would stop receiving that value.
Secondly, Marketing Cloud has 2 sets of API limitations, yearly and minute by minute. Depending on your licensing, you could run into the yearly limit.
The problem you have with limitation on minutes (2500 for REST and 2000 for SOAP) is that each usage of the custom activity in journey builder would multiple the amount of invocations per minute. Hitting this limit would cause issues for incremental data flows into SFMC.
I'd also suggest not retrieving any data from Marketing Cloud when a customer gets sent through a custom activity. Users should pick which corresponding rows/data that should be sent to the custom activity in their segmentation.
The eventDefinitionKey can be picked up from postmonger after requestedTriggerEventDefinition in the eventDefinitionModel function. eventDefinitionKey can then be used to programmatically populate SFMC's POST call with data from the Journey Data model, thus allowing marketers to select what data to be sent with the subscriber.
Following is some code to show how it would work in your customActivity.js
connection.on(
'requestedTriggerEventDefinition',
function (eventDefinitionModel) {
var eventKey = eventDefinitionModel['eventDefinitionKey'];
save(eventKey);
}
);
function save(eventKey) {
// subscriberKey fetched directly from Contact model
// columnName is populated from the Journey Data model
var params = {
subscriberKey: '{{Contact.key}}',
columnName: '{{Event.' + eventKey + '.columnName}}',
};
payload['arguments'].execute.inArguments = [params];
}
I'm actually creating my first app using meteor, in particular using angular 2. I've experience with Angular 1 and 2, so based on it. I've some points of concern...
Let's imagine this scenario...My data stored on MongoDb:
Collection: clients
{
name : "Happy client",
password : "Something non encrypted",
fullCrediCardNumber : "0000 0000 0000 0000"
}
Now, on my meteor client folder, I've this struncture...
collection clients.ts (server folder)
export var Clients = new Mongo.Collection('clients');
component client.ts (not server folder)
import {Clients} from '../collections/clients.ts';
class MyClients {
clients: Array<Object>;
constructor(zone: NgZone) {
this.clients = Clients.find();
}
}
..and for last: the html page to render it, but just display the name of the clients:
<li *ngFor="#item of clients">
{{client.name}}
</li>
Ok so far. but my concern is: In angular 1 & 2 applications the component or controller or directive runs on the client side, not server side.
I set my html just to show the name of the client. but since it's ah html rendering, probably with some skill is pretty easy to inject some code into the HTML render on angular to display all my fields.
Or could be easy to go to the console and type some commands to display the entire object from the database collection.
So, my question is: How safe meteor is in this sense ? Does my concerns correct ? Is meteor capable to protect my data , protect the name of the collections ? I know that I can specify on the find() to not bring me those sensitive data, but since the find() could be running not on the server side, it could be easy to modify it on the fly, no ?
Anyway...I will appreciate explanations about how meteor is safe (or not) in this sense.
ty !
You can protect data by simply not publishing any sensitive data on the server side.
Meteor.publish("my-clients", function () {
return Clients.find({
contractorId: this.userId // Publish only the current user's clients
}, {
name: 1, // Publish only the fields you want the browser to know of
phoneNumber: 1
});
});
This example only publishes the name and address fields of the currently logged in user's clients, but not their password or fullCreditCardNumber.
Another good example is the Meteor.users collection. On the server it contains all user data, login credentials, profiles etc. for all users. But it's also accessible on the client side. Meteor does two important things to protect this very sensitive collection:
By default it only publishes one document: the user that's logged in. If you type Meteor.users.find().fetch() into the browser console, you'll only see the currently logged in user's data, and there's no way on the client side to get the entire MongoDB users collection. The correct way to do this is to restrict the amount of published documents in your Meteor.publish function. See my example above, or 10.9 in the Meteor publish and subscribe tutorial.
Not the entire user document gets published. For example OAuth login credentials and password hashes aren't, you won't find them in the client-side collection. You can always choose which part of a document gets published, a simple way to do that is using MongoDB projections, like in the example above.
Does anyone know how to delete all datastore in Google App Engine?
If you're talking about the live datastore, open the dashboard for your app (login on appengine) then datastore --> dataviewer, select all the rows for the table you want to delete and hit the delete button (you'll have to do this for all your tables).
You can do the same programmatically through the remote_api (but I never used it).
If you're talking about the development datastore, you'll just have to delete the following file: "./WEB-INF/appengine-generated/local_db.bin". The file will be generated for you again next time you run the development server and you'll have a clear db.
Make sure to clean your project afterwards.
This is one of the little gotchas that come in handy when you start playing with the Google Application Engine. You'll find yourself persisting objects into the datastore then changing the JDO object model for your persistable entities ending up with obsolete data that'll make your app crash all over the place.
The best approach is the remote API method as suggested by Nick, he's an App Engine engineer from Google, so trust him.
It's not that difficult to do, and the latest 1.2.5 SDK provides the remote_shell_api.py out of the shelf. So go to download the new SDK. Then follow the steps:
connect remote server in your commandline: remote_shell_api.py yourapp /remote_api
The shell will ask for your login info, and if authorized, will make a Python shell for you. You need setup url handler for /remote_api in your app.yaml
fetch the entities you'd like to delete, the code looks something like:
from models import Entry
query = Entry.all(keys_only=True)
entries =query.fetch(1000)
db.delete(entries)
\# This could bulk delete 1000 entities a time
Update 2013-10-28:
remote_shell_api.py has been replaced by remote_api_shell.py, and you should connect with remote_api_shell.py -s your_app_id.appspot.com, according to the documentation.
There is a new experimental feature Datastore Admin, after enabling it in app settings, you can bulk delete as well as backup your datastore through the web ui.
The fastest and efficient way to handle bulk delete on Datastore is by using the new mapper API announced on the latest Google I/O.
If your language of choice is Python, you just have to register your mapper in a mapreduce.yaml file and define a function like this:
from mapreduce import operation as op
def process(entity):
yield op.db.Delete(entity)
On Java you should have a look to this article that suggests a function like this:
#Override
public void map(Key key, Entity value, Context context) {
log.info("Adding key to deletion pool: " + key);
DatastoreMutationPool mutationPool = this.getAppEngineContext(context)
.getMutationPool();
mutationPool.delete(value.getKey());
}
EDIT:
Since SDK 1.3.8, there's a Datastore admin feature for this purpose
You can clear the development server datastore when you run the server:
/path/to/dev_appserver.py --clear_datastore=yes myapp
You can also abbreviate --clear_datastore with -c.
If you have a significant amount of data, you need to use a script to delete it. You can use remote_api to clear the datastore from the client side in a straightforward manner, though.
Here you go: Go to Datastore Admin, and then select the Entity type you want to delete and click Delete. Mapreduce will take care of deleting!
There are several ways you can use to remove entries from App Engine's Datastore:
First, think whether you really need to remove entries. This is expensive and it might be cheaper to not remove them.
You can delete all entries by hand using the Datastore Admin.
You can use the Remote API and remove entries interactively.
You can remove the entries programmatically using a couple lines of code.
You can remove them in bulk using Task Queues and Cursors.
Or you can use Mapreduce to get something more robust and fancier.
Each one of these methods is explained in the following blog post:
http://www.shiftedup.com/2015/03/28/how-to-bulk-delete-entries-in-app-engine-datastore
Hope it helps!
The zero-setup way to do this is to send an execute-arbitrary-code HTTP request to the admin service that your running app already, automatically, has:
import urllib
import urllib2
urllib2.urlopen('http://localhost:8080/_ah/admin/interactive/execute',
data = urllib.urlencode({'code' : 'from google.appengine.ext import db\n' +
'db.delete(db.Query())'}))
Source
I got this from http://code.google.com/appengine/articles/remote_api.html.
Create the Interactive Console
First, you need to define an interactive appenginge console. So, create a file called appengine_console.py and enter this:
#!/usr/bin/python
import code
import getpass
import sys
# These are for my OSX installation. Change it to match your google_appengine paths. sys.path.append("/Applications/GoogleAppEngineLauncher.app/Contents/Resources/GoogleAppEngine-default.bundle/Contents/Resources/google_appengine")
sys.path.append("/Applications/GoogleAppEngineLauncher.app/Contents/Resources/GoogleAppEngine-default.bundle/Contents/Resources/google_appengine/lib/yaml/lib")
from google.appengine.ext.remote_api import remote_api_stub
from google.appengine.ext import db
def auth_func():
return raw_input('Username:'), getpass.getpass('Password:')
if len(sys.argv) < 2:
print "Usage: %s app_id [host]" % (sys.argv[0],)
app_id = sys.argv[1]
if len(sys.argv) > 2:
host = sys.argv[2]
else:
host = '%s.appspot.com' % app_id
remote_api_stub.ConfigureRemoteDatastore(app_id, '/remote_api', auth_func, host)
code.interact('App Engine interactive console for %s' % (app_id,), None, locals())
Create the Mapper base class
Once that's in place, create this Mapper class. I just created a new file called utils.py and threw this:
class Mapper(object):
# Subclasses should replace this with a model class (eg, model.Person).
KIND = None
# Subclasses can replace this with a list of (property, value) tuples to filter by.
FILTERS = []
def map(self, entity):
"""Updates a single entity.
Implementers should return a tuple containing two iterables (to_update, to_delete).
"""
return ([], [])
def get_query(self):
"""Returns a query over the specified kind, with any appropriate filters applied."""
q = self.KIND.all()
for prop, value in self.FILTERS:
q.filter("%s =" % prop, value)
q.order("__key__")
return q
def run(self, batch_size=100):
"""Executes the map procedure over all matching entities."""
q = self.get_query()
entities = q.fetch(batch_size)
while entities:
to_put = []
to_delete = []
for entity in entities:
map_updates, map_deletes = self.map(entity)
to_put.extend(map_updates)
to_delete.extend(map_deletes)
if to_put:
db.put(to_put)
if to_delete:
db.delete(to_delete)
q = self.get_query()
q.filter("__key__ >", entities[-1].key())
entities = q.fetch(batch_size)
Mapper is supposed to be just an abstract class that allows you to iterate over every entity of a given kind, be it to extract their data, or to modify them and store the updated entities back to the datastore.
Run with it!
Now, start your appengine interactive console:
$python appengine_console.py <app_id_here>
That should start the interactive console. In it create a subclass of Model:
from utils import Mapper
# import your model class here
class MyModelDeleter(Mapper):
KIND = <model_name_here>
def map(self, entity):
return ([], [entity])
And, finally, run it (from you interactive console):
mapper = MyModelDeleter()
mapper.run()
That's it!
You can do it using the web interface. Login into your account, navigate with links on the left hand side. In Data Store management you have options to modify and delete data. Use respective options.
I've created an add-in panel that can be used with your deployed App Engine apps. It lists the kinds that are present in the datastore in a dropdown, and you can click a button to schedule "tasks" that delete all entities of a specific kind or simply everything. You can download it here:
http://code.google.com/p/jobfeed/wiki/Nuke
For Python, 1.3.8 includes an experimental admin built-in for this. They say: "enable the following builtin in your app.yaml file:"
builtins:
- datastore_admin: on
"Datastore delete is currently available only with the Python runtime. Java applications, however, can still take advantage of this feature by creating a non-default Python application version that enables Datastore Admin in the app.yaml. Native support for Java will be included in an upcoming release."
Open "Datastore Admin" for your application and enable Admin. Then all of your entities will be listed with check boxes. You can simply select the unwanted entites and delete them.
This is what you're looking for...
db.delete(Entry.all(keys_only=True))
Running a keys-only query is much faster than a full fetch, and your quota will take a smaller hit because keys-only queries are considered small ops.
Here's a link to an answer from Nick Johnson describing it further.
Below is an end-to-end REST API solution to truncating a table...
I setup a REST API to handle database transactions where routes are directly mapped through to the proper model/action. This can be called by entering the right url (example.com/inventory/truncate) and logging in.
Here's the route:
Route('/inventory/truncate', DataHandler, defaults={'_model':'Inventory', '_action':'truncate'})
Here's the handler:
class DataHandler(webapp2.RequestHandler):
#basic_auth
def delete(self, **defaults):
model = defaults.get('_model')
action = defaults.get('_action')
module = __import__('api.models', fromlist=[model])
model_instance = getattr(module, model)()
result = getattr(model_instance, action)()
It starts by loading the model dynamically (ie Inventory found under api.models), then calls the correct method (Inventory.truncate()) as specified in the action parameter.
The #basic_auth is a decorator/wrapper that provides authentication for sensitive operations (ie POST/DELETE). There's also an oAuth decorator available if you're concerned about security.
Finally, the action is called:
def truncate(self):
db.delete(Inventory.all(keys_only=True))
It looks like magic but it's actually very straightforward. The best part is, delete() can be re-used to handle deleting one-or-many results by adding another action to the model.
You can Delete All Datastore by deleting all Kinds One by One.
with google appengine dash board. Please follow these Steps.
Login to https://console.cloud.google.com/datastore/settings
Click Open Datastore Admin. (Enable it if not enabled.)
Select all Entities and press delete.(This Step run a map reduce job for deleting all selected Kinds.)
for more information see This image http://storage.googleapis.com/bnifsc/Screenshot%20from%202015-01-31%2023%3A58%3A41.png
If you have a lot of data, using the web interface could be time consuming. The App Engine Launcher utility lets you delete everything in one go with the 'Clear datastore on launch' checkbox. This utility is now available for both Windows and Mac (Python framework).
For the development server, instead of running the server through the google app engine launcher, you can run it from the terminal like:
dev_appserver.py --port=[portnumber] --clear_datastore=yes [nameofapplication]
ex: my application "reader" runs on port 15080. After modify the code and restart the server, I just run "dev_appserver.py --port=15080 --clear_datastore=yes reader".
It's good for me.
Adding answer about recent developments.
Google recently added datastore admin feature. You can backup, delete or copy your entities to another app using this console.
https://developers.google.com/appengine/docs/adminconsole/datastoreadmin#Deleting_Entities_in_Bulk
I often don't want to delete all the data store so I pull a clean copy of /war/WEB-INF/local_db.bin out source control. It may just be me but it seems even with the Dev Mode stopped I have to physically remove the file before pulling it. This is on Windows using the subversion plugin for Eclipse.
PHP variation:
import com.google.appengine.api.datastore.Query;
import com.google.appengine.api.datastore.DatastoreServiceFactory;
define('DATASTORE_SERVICE', DatastoreServiceFactory::getDatastoreService());
function get_all($kind) {
$query = new Query($kind);
$prepared = DATASTORE_SERVICE->prepare($query);
return $prepared->asIterable();
}
function delete_all($kind, $amount = 0) {
if ($entities = get_all($kind)) {
$r = $t = 0;
$delete = array();
foreach ($entities as $entity) {
if ($r < 500) {
$delete[] = $entity->getKey();
} else {
DATASTORE_SERVICE->delete($delete);
$delete = array();
$r = -1;
}
$r++; $t++;
if ($amount && $amount < $t) break;
}
if ($delete) {
DATASTORE_SERVICE->delete($delete);
}
}
}
Yes it will take time and 30 sec. is a limit. I'm thinking to put an ajax app sample to automate beyond 30 sec.
for amodel in db.Model.__subclasses__():
dela=[]
print amodel
try:
m = amodel()
mq = m.all()
print mq.count()
for mw in mq:
dela.append(mw)
db.delete(dela)
#~ print len(dela)
except:
pass
If you're using ndb, the method that worked for me for clearing the datastore:
ndb.delete_multi(ndb.Query(default_options=ndb.QueryOptions(keys_only=True)))
For any datastore that's on app engine, rather than local, you can use the new Datastore API. Here's a primer for how to get started.
I wrote a script that deletes all non-built in entities. The API is changing pretty rapidly, so for reference, I cloned it at commit 990ab5c7f2063e8147bcc56ee222836fd3d6e15b
from gcloud import datastore
from gcloud.datastore import SCOPE
from gcloud.datastore.connection import Connection
from gcloud.datastore import query
from oauth2client import client
def get_connection():
client_email = 'XXXXXXXX#developer.gserviceaccount.com'
private_key_string = open('/path/to/yourfile.p12', 'rb').read()
svc_account_credentials = client.SignedJwtAssertionCredentials(
service_account_name=client_email,
private_key=private_key_string,
scope=SCOPE)
return Connection(credentials=svc_account_credentials)
def connect_to_dataset(dataset_id):
connection = get_connection()
datastore.set_default_connection(connection)
datastore.set_default_dataset_id(dataset_id)
if __name__ == "__main__":
connect_to_dataset(DATASET_NAME)
gae_entity_query = query.Query()
gae_entity_query.keys_only()
for entity in gae_entity_query.fetch():
if entity.kind[0] != '_':
print entity.kind
entity.key.delete()
continuing the idea of svpino it is wisdom to reuse records marked as delete. (his idea was not to remove, but mark as "deleted" unused records). little bit of cache/memcache to handle working copy and write only difference of states (before and after desired task) to datastore will make it better. for big tasks it is possible to write itermediate difference chunks to datastore to avoid data loss if memcache disappeared. to make it loss-proof it is possible to check integrity/existence of memcached results and restart task (or required part) to repeat missing computations. when data difference is written to datastore, required computations are discarded in queue.
other idea similar to map reduced is to shard entity kind to several different entity kinds, so it will be collected together and visible as single entity kind to final user. entries are only marked as "deleted". when "deleted" entries amount per shard overcomes some limit, "alive" entries are distributed between other shards, and this shard is closed forever and then deleted manually from dev console (guess at less cost) upd: seems no drop table at console, only delete record-by-record at regular price.
it is possible to delete by query by chunks large set of records without gae failing (at least works locally) with possibility to continue in next attempt when time is over:
qdelete.getFetchPlan().setFetchSize(100);
while (true)
{
long result = qdelete.deletePersistentAll(candidates);
LOG.log(Level.INFO, String.format("deleted: %d", result));
if (result <= 0)
break;
}
also sometimes it useful to make additional field in primary table instead of putting candidates (related records) into separate table. and yes, field may be unindexed/serialized array with little computation cost.
For all people that need a quick solution for the dev server (as time of writing in Feb. 2016):
Stop the dev server.
Delete the target directory.
Rebuild the project.
This will wipe all data from the datastore.
I was so frustrated about existing solutions for deleting all data in the live datastore that I created a small GAE app that can delete quite some amount of data within its 30 seconds.
How to install etc: https://github.com/xamde/xydra
For java
DatastoreService db = DatastoreServiceFactory.getDatastoreService();
List<Key> keys = new ArrayList<Key>();
for(Entity e : db.prepare(new Query().setKeysOnly()).asIterable())
keys.add(e.getKey());
db.delete(keys);
Works well in Development Server
You have 2 simple ways,
#1: To save cost, delete the entire project
#2: using ts-datastore-orm:
https://www.npmjs.com/package/ts-datastore-orm
await Entity.truncate();
The truncate can delete around 1K rows per seconds
Here's how I did this naively from a vanilla Google Cloud Shell (no GAE) with python3:
from google.cloud import datastore
client = datastore.Client()
query.keys_only()
for counter, entity in enumerate(query.fetch()):
if entity.kind.startswith('_'): # skip reserved kinds
continue
print(f"{counter}: {entity.key}")
client.delete(entity.key)
This takes a very long time even with a relatively small amount of keys but it works.
More info about the Python client library: https://googleapis.dev/python/datastore/latest/client.html
As of 2022, there are two ways to delete a kind from a (largeish) datastore to the best of my knowledge. Google recommends using a Dataflow template. The template will basically pull each entity one by one subject to a GQL query, and then delete it. Interestingly, if you are deleting a large number of rows (> 10m), you will run into datastore troubles; as it will fail to provide enough capacity, and your operations to the datastore will start timing out. However, only the kind you are mass deleting from will be effected.
If you have less than 10m rows, you can just use this go script:
import (
"cloud.google.com/go/datastore"
"context"
"fmt"
"google.golang.org/api/option"
"log"
"strings"
"sync"
"time"
)
const (
batchSize = 10000 // number of keys to get in a single batch
deleteBatchSize = 500 // number of keys to delete in a single batch
projectID = "name-of-your-GCP-project"
serviceAccount = "path-to-sa-file"
table = "kind-to-delete"
)
func min(a, b int) int {
if a < b {
return a
}
return b
}
func deleteBatch(table string) int {
ctx := context.Background()
client, err := datastore.NewClient(ctx, projectID, option.WithCredentialsFile(serviceAccount))
if err != nil {
log.Fatalf("Failed to open client: %v", err)
}
defer client.Close()
query := datastore.NewQuery(table).KeysOnly().Limit(batchSize)
keys, err := client.GetAll(ctx, query, nil)
if err != nil {
fmt.Printf("%s Failed to get %d keys : %v\n", table, batchSize, err)
return -1
}
var wg sync.WaitGroup
for i := 0; i < len(keys); i += deleteBatchSize {
wg.Add(1)
go func(i int) {
batch := keys[i : i+min(len(keys)-i, deleteBatchSize)]
if err := client.DeleteMulti(ctx, batch); err != nil {
// not a big problem, we'll get them next time ;)
fmt.Printf("%s Failed to delete multi: %v", table, err)
}
wg.Done()
}(i)
}
wg.Wait()
return len(keys)
}
func main() {
var globalStartTime = time.Now()
fmt.Printf("Deleting \033[1m%s\033[0m\n", table)
for {
startTime := time.Now()
count := deleteBatch(table)
if count >= 0 {
rate := float64(count) / time.Since(startTime).Seconds()
fmt.Printf("Deleted %d keys from %s in %.2fs, rate %.2f keys/s\n", count, table, time.Since(startTime).Seconds(), rate)
if count == 0 {
fmt.Printf("%s is now clear.\n", table)
break
}
} else {
fmt.Printf("Retrying after short cooldown\n")
time.Sleep(10 * time.Second)
}
}
fmt.Printf("Total time taken %s.\n", time.Since(globalStartTime))
}