How to delete all datastore in Google App Engine? - google-app-engine

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remote_shell_api.py has been replaced by remote_api_shell.py, and you should connect with remote_api_shell.py -s your_app_id.appspot.com, according to the documentation.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))
}

Related

Creating a cluster before sending a job to dataproc programmatically

I'm trying to schedule a PySpark Job. I followed the GCP documentation and ended up deploying a little python script to App Engine which does the following :
authenticate using a service account
submit a job to a cluster
The problem is, I need the cluster to be up and running otherwise the job won't be sent (duh !) but I don't want the cluster to always be up and running, especially since my job needs to run once a month.
I wanted to add the creation of a cluster in my python script but the call is asynchronous (it makes an HTTP request) and thus my job is submitted after the cluster creation call but before the cluster is really up and running.
How could I do ?
I'd like something cleaner than just waiting for a few minutes in my script !
Thanks
EDIT : Here's what my code looks like so far :
To launch the job
class EnqueueTaskHandler(webapp2.RequestHandler):
def get(self):
task = taskqueue.add(
url='/run',
target='worker')
self.response.write(
'Task {} enqueued, ETA {}.'.format(task.name, task.eta))
app = webapp2.WSGIApplication([('/launch', EnqueueTaskHandler)], debug=True)
The job
class CronEventHandler(webapp2.RequestHandler):
def create_cluster(self, dataproc, project, zone, region, cluster_name):
zone_uri = 'https://www.googleapis.com/compute/v1/projects/{}/zones/{}'.format(project, zone)
cluster_data = {...}
dataproc.projects().regions().clusters().create(
projectId=project,
region=region,
body=cluster_data).execute()
def wait_for_cluster(self, dataproc, project, region, clustername):
print('Waiting for cluster to run...')
while True:
result = dataproc.projects().regions().clusters().get(
projectId=project,
region=region,
clusterName=clustername).execute()
# Handle exceptions
if result['status']['state'] != 'RUNNING':
time.sleep(60)
else:
return result
def wait_for_job(self, dataproc, project, region, job_id):
print('Waiting for job to finish...')
while True:
result = dataproc.projects().regions().jobs().get(
projectId=project,
region=region,
jobId=job_id).execute()
# Handle exceptions
print(result['status']['state'])
if result['status']['state'] == 'ERROR' or result['status']['state'] == 'DONE':
return result
else:
time.sleep(60)
def submit_job(self, dataproc, project, region, clusterName):
job = {...}
result = dataproc.projects().regions().jobs().submit(projectId=project,region=region,body=job).execute()
return result['reference']['jobId']
def post(self):
dataproc = googleapiclient.discovery.build('dataproc', 'v1')
project = '...'
region = "..."
zone = "..."
clusterName = '...'
self.create_cluster(dataproc, project, zone, region, clusterName)
self.wait_for_cluster(dataproc, project, region, clusterName)
job_id = self.submit_job(dataproc,project,region,clusterName)
self.wait_for_job(dataproc,project,region,job_id)
dataproc.projects().regions().clusters().delete(projectId=project, region=region, clusterName=clusterName).execute()
self.response.write("JOB SENT")
app = webapp2.WSGIApplication([('/run', CronEventHandler)], debug=True)
Everything works until the deletion of the cluster. At this point I get a "DeadlineExceededError: The overall deadline for responding to the HTTP request was exceeded." Any idea ?
In addition to general polling either through list or get requests on the Cluster or the Operation returned with the CreateCluster request, for single-use clusters like this you can also consider using the Dataproc Workflows API and possibly its InstantiateInline interface if you don't want to use full-fledged workflow templates; in this API you use a single request to specify cluster settings along with jobs to submit, and the jobs will automatically run as soon as the cluster is ready to take it, after which the cluster will be deleted automatically.
You can use the Google Cloud Dataproc API to create, delete and list clusters.
The list operation can be (repeatedly) performed after create and delete operations to confirm that they completed successfully, since it provides the ClusterStatus of the clusters in the results with the relevant State information:
UNKNOWN The cluster state is unknown.
CREATING The cluster is being created and set up. It is not ready for use.
RUNNING The cluster is currently running and healthy. It is ready for use.
ERROR The cluster encountered an error. It is not ready for use.
DELETING The cluster is being deleted. It cannot be used.
UPDATING The cluster is being updated. It continues to accept and process jobs.
To prevent plain waiting between the (repeated) list invocations (in general not a good thing to do on GAE) you can enqueue delayed tasks in a push task queue (with the relevant context information) allowing you to perform such list operations at a later time. For example, in python, see taskqueue.add():
countdown -- Time in seconds into the future that this task should run or be leased. Defaults to zero. Do not specify this argument if
you specified an eta.
eta -- A datetime.datetime that specifies the absolute earliest time at which the task should run. You cannot specify this argument if
the countdown argument is specified. This argument can be time
zone-aware or time zone-naive, or set to a time in the past. If the
argument is set to None, the default value is now. For pull tasks, no
worker can lease the task before the time indicated by the eta
argument.
If at the task execution time the result indicates the operation of interest is still in progress simply enqueue another such delayed task - effectively polling but without an actual wait/sleep.

Deleting massive of entities from Google App Engine NDB

The previous guys made som problem in our Google App Engine app. Currently, the app is saving entities with NULL values, but it would be better if we could clean up all thees values.
Here is the ndb.Modal:
class Day(ndb.Model):
date = ndb.DateProperty(required=True, indexed=True)
items = ndb.StringProperty(repeated=True, indexed=False)
reason = ndb.StringProperty(name="cancelled", indexed=False)
is_hole = ndb.ComputedProperty(lambda s: not bool(s.items or s.reason))
Somehow, we need to delete all Days where is_hole is true.
It's around 4 000 000 entities where around 2 000 000 should be deleted on the server.
Code so far
I thought it would be good to first count how many entities we should delete using this code:
count = Day.query(Day.is_hole != False).count(10000)
This (with the limit of 10 000) takes around 5 seconds to run. Without the limit, it would case a DeadLineException.
For deleting, I've tried this code:
ndb.delete_multi([key for key in Day.query(Day.is_hole != False).fetch(10000, keys_only=True)])
This (with the limit) takes around 30 seconds.
Question
How can I faster delete all Day where is_hole != False?
(We are using Python)
No, there is not faster way to delete entities - deadline is fixed.
But there are some tricks.
You can make deadline longer if you will use https://cloud.google.com/appengine/docs/python/taskqueue/ you can put some task in queue generate next task after first task (recurrence).
Another option similar to task queue is to make after deleting some of bad record redirect to same handler which is deleting while the last record will be deleted. Need browser open till the end.
if at_least_one_bad_record:
delete_some_records (not longer than 30s)
spawn again this task or redirect to this handler (next call will have next 30s)
Remember that it has exit point if no more good records. It will delete all matching record without clicking again.
Best way is to use MapReduce which will run in task queue and also you can do sharding to parallel the work. Here is the python code. Let me know, if you need any clarification
main.py
from mapreduce import base_handler
from mapreduce import mapreduce_pipeline
from mapreduce import operation as op
from mapreduce.input_readers import InputReader
from google.appengine.api import app_identity
def deleteEntity(entity):
yield op.db.Delete(entity)
class DeleteEntitiesPipeline(base_handler.PipelineBase):
def run(self):
bucket_name = (app_identity.get_default_gcs_bucket_name())
yield mapreduce_pipeline.MapPipeline(
"job_name",
"main.deleteEntity",
"mapreduce.input_readers.DatastoreInputReader",
params={
"entity_kind": 'models.Day',
"filters": [("is_hole", "=", True)],
"bucket_name": bucket_name
},
shards=5)
class StartDelete(webapp2.RequestHandler):
def get(self):
pipeline = DeleteEntitiesPipeline()
pipeline.start()
application = webapp2.WSGIApplication([
('/deleteentities', StartDelete),
], debug=True)

Google App Engine Datastore Migration

I have a CSV file of this form:
Username, Password_Hash
noam , ************
paz , ************
I want to import this CSV into my datastore so the data could be accessed from python by using this model:
class Company(ndb.Model):
Username = ndb.StringProperty()
Password_Hash= ndb.StringProperty(indexed=False)
Of course, manual import one by one is not an option because the real file is pretty large.
I've no idea of which structure the file used by gcloud preview datastore upload is based on.
Google has a lack of good documentation on this issue.
How about something like:
from google.appengine.api import urlfetch
from models import Company
def do_it(request):
csv_string = 'http://mysite-or-localhost/table.csv'
csv_response = urlfetch.fetch(csv_string, allow_truncated=True)
if csv_response.status_code == 200:
for row in csv_response.content.split('\n'):
if row != '' and not row.lower().startswith('Username,'):
row_values = row.split(',')
new_record = Company(
Username = row_values[0],
Password_Hash = row_values[1]
)
new_record.put()
return Response("Did it", mimetype='text/plain')
there is no magic way of migrating. you need to write a program that reads the file and saves to the datastore one by one. it's not particularly difficult to write this program. give it as long as it takes, it won't be forever...

What response times can be expected from GAE/NDB?

We are currently building a small and simple central HTTP service that maps "external identities" (like a facebook id) to an "internal (uu)id", unique across all our services to help with analytics.
The first prototype in "our stack" (flask+postgresql) was done within a day. But since we want the service to (almost) never fail and scale automagically, we decided to use Google App Engine.
After a week of reading&trying&benchmarking this question emerges:
What response times are considered "normal" on App Engine (with NDB)?
We are getting response times that are consistently above 500ms on average and well above 1s in the 90percentile.
I've attached a stripped down version of our code below, hoping somebody can point out the obvious flaw. We really like the autoscaling and the distributed storage, but we can not imagine 500ms really is the expected performance in our case. The sql based prototype responded much faster (consistently), hosted on one single Heroku dyno using the free, cache-less postgresql (even with an ORM).
We tried both synchronous and asynchronous variants of the code below and looked at the appstats profile. It's always RPC calls (both memcache and datastore) that take very long (50ms-100ms), made worse by the fact that there are always multiple calls (eg. mc.get() + ds.get() + ds.set() on a write). We also tried deferring as much as possible to the task queue, without noticeable gains.
import json
import uuid
from google.appengine.ext import ndb
import webapp2
from webapp2_extras.routes import RedirectRoute
def _parse_request(request):
if request.content_type == 'application/json':
try:
body_json = json.loads(request.body)
provider_name = body_json.get('provider_name', None)
provider_user_id = body_json.get('provider_user_id', None)
except ValueError:
return webapp2.abort(400, detail='invalid json')
else:
provider_name = request.params.get('provider_name', None)
provider_user_id = request.params.get('provider_user_id', None)
return provider_name, provider_user_id
class Provider(ndb.Model):
name = ndb.StringProperty(required=True)
class Identity(ndb.Model):
user = ndb.KeyProperty(kind='GlobalUser')
class GlobalUser(ndb.Model):
uuid = ndb.StringProperty(required=True)
#property
def identities(self):
return Identity.query(Identity.user==self.key).fetch()
class ResolveHandler(webapp2.RequestHandler):
#ndb.toplevel
def post(self):
provider_name, provider_user_id = _parse_request(self.request)
if not provider_name or not provider_user_id:
return self.abort(400, detail='missing provider_name and/or provider_user_id')
identity = ndb.Key(Provider, provider_name, Identity, provider_user_id).get()
if identity:
user_uuid = identity.user.id()
else:
user_uuid = uuid.uuid4().hex
GlobalUser(
id=user_uuid,
uuid=user_uuid
).put_async()
Identity(
parent=ndb.Key(Provider, provider_name),
id=provider_user_id,
user=ndb.Key(GlobalUser, user_uuid)
).put_async()
return webapp2.Response(
status='200 OK',
content_type='application/json',
body = json.dumps({
'provider_name' : provider_name,
'provider_user_id' : provider_user_id,
'uuid' : user_uuid
})
)
app = webapp2.WSGIApplication([
RedirectRoute('/v1/resolve', ResolveHandler, 'resolve', strict_slash=True)
], debug=False)
For completeness sake the (almost default) app.yaml
application: GAE_APP_IDENTIFIER
version: 1
runtime: python27
api_version: 1
threadsafe: yes
handlers:
- url: .*
script: main.app
libraries:
- name: webapp2
version: 2.5.2
- name: webob
version: 1.2.3
inbound_services:
- warmup
In my experience, RPC performance fluctuates by orders of magnitude, between 5ms-100ms for a datastore get. I suspect it's related to the GAE datacenter load. Sometimes it gets better, sometimes it gets worse.
Your operation looks very simple. I expect that with 3 requests, it should take about 20ms, but it could be up to 300ms. A sustained average of 500ms sounds very high though.
ndb does local caching when fetching objects by ID. That should kick in if you're accessing the same users, and those requests should be much faster.
I assume you're doing perf testing on the production and not dev_appserver. dev_appserver performance is not representative.
Not sure how many iterations you've tested, but you might want to try a larger number to see if 500ms is really your average.
When you're blocked on simple RPC calls, there's not too optimizing you can do.
The 1st obvious moment I see: do you really need a transaction on every request?
I believe that unless most of your requests create new entities it's better to do .get_by_id() outside of transaction. And if entity not found then start transaction or even better defer creation of the entity.
def request_handler(key, data):
entity = key.get()
if entity:
return 'ok'
else:
defer(_deferred_create, key, data)
return 'ok'
def _deferred_create(key, data):
#ndb.transactional
def _tx():
entity = key.get()
if not entity:
entity = CreateEntity(data)
entity.put()
_tx()
That should give much better response time for user facing requests.
The 2nd and only optimization I see is to use ndb.put_multi() to minimize RPC calls.
P.S. Not 100% sure but you can try to disable multithreading (threadsave: no) to get more stable response time.

'IntegrityError: column username is not unique' while using Django User model in test

While running some tests, I started to get an IntegrityError in my setUp function. Here is my code:
def setUp(self):
self.client = Client()
self.emplUser = User.objects.create_user('employee#email.com', 'employee#email.com', 'nothing')
self.servUser1 = User.objects.create_user('thebestcompany#email.com', 'thebestcompany#email.com', 'nothing')
self.servUser2 = User.objects.create_user('theothercompany#email.com', 'theothercompany#email.com', 'nothing')
self.custUser1 = User.objects.create_user('john#email.com', 'john#email.com', 'nothing')
self.custUser2 = User.objects.create_user('marcus#email.com', 'marcus#email.com', 'nothing')
... save users here ...
Im wondering as to how this IntegrityError keeps getting raised. I delete all the users in the tearDown function and am using sqlite3 as my DB backend. I see no conflicting usernames and in production, I have no issues with using emails as usernames.
This started happening only half an hour ago, out of the blue. Has anyone run into a solution to this problem?
I'm sure you're not suffering this problem anymore since you wrote 18 months ago, but I had this problem too, and finally figured out what was happening. When using Postgres for test cases, DB changes are done in a transaction and simply rolled back, and so it is not necessary to explicitly clear tables in tearDown(), however, in SQLite, it is necessary.
Late but more appropriate answer, for the people who would land there after a google search:
When there is interaction with the database in your tests (typically, creating model instances), you should subclass your test class from django.test.TestCase, which flushes the database after each test is run.
Then you don't need to write a tedious tearDown method in all your test classes.
See https://docs.djangoproject.com/en/dev/topics/testing/overview/#writing-tests

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