I'm using mongodb to store some data. Then I have a function that gets the object with the latest timestamp and one with the oldest. I haven't experienced any issues during development or production with this method but when I try to implement a test for it the test fails approx 20% of the times. I'm using rspec to test this method and I'm not using mongoid or mongomapper. I create three objects with different timestamps but get a nil response since my dataset contains 0 objects. I have read a lot of articles about write_concern and that it might be the problem with "unsafe writes" but I have tried almost all the different combinations with these parameters (w, fsync, j, wtimeout) without any success. Does anyone have any idea how to solve this issue? Perhaps I have focused too much with the write_concern track and that the problems lies somewhere else.
This is the method that fetches the latest and oldest timestamp.
def first_and_last_timestamp(customer_id, system_id)
last = collection(customer_id).
find({sid:system_id}).
sort(["t",Mongo::DESCENDING]).
limit(1).next()
first = collection(customer_id).
find({sid:system_id}).
sort(["t",Mongo::ASCENDING]).
limit(1).next()
{ min: first["t"], max: last["t"] }
end
Im inserting data using this method where data is a json object.
def insert(customer_id, data)
collection(customer_id).insert(data)
end
I have reverted back to use the default for setting up my connection
Mongo::MongoClient.new(mongo_host, mongo_port)
I'm using the gem mongo (1.10.2). I'm not using any fancy setup for my mongo database. I've just installed mongo using brew on my mac and started it. The version of my mongo database is v2.6.1.
Related
I'm trying to prototype using the SmartRedis Python client to interact with the SmartSim Orchestrator. Is it possible to launch the orchestrator without any other models in the experiment? If so, what would be the best way to do so?
It is entirely possible to do that. A SmartSim Experiment can contain different types of 'entities' including Models, Ensembles (i.e. groups of Models), and Orchestrator (i.e. the Redis-backed database). None of these entities, however, are 'required' to be in the Experiment.
Here's a short script that creates an experiment which includes only a database.
from SmartSim import Experiment
NUM_DB_NODES = 3
exp = Experiment("Database Only")
db = exp.create_database(db_nodes=NUM_DB_NODES)
exp.generate(db)
exp.start(db)
After this, the Orchestrator (with the number of shards specified by NUM_DB_NODES) will have been spunup. You can then connect the Python client using the following line:
client = smartredis.Client(db.get_address()[0],NUM_DB_NODES>1)
I am working with spark version 3.0.1. I am generating a large dataframe. At the end calculations, I save dataframe plan in json format. I need him.
But there is one problem. If I persist a DataFrame, then its plan in json format is completely truncated. That is, all data lineage disappears.
For example, I do this:
val myDf: DataFrame = ???
val myPersistDf = myDf.persist
//toJSON method cuts down my plan
val jsonPlan = myPersistDf.queryExecution.optimizedPlan.toJSON
As a result, only information about the current columns remains.
But for example, if you use spark version 3.1.2, then there is no such problem. That is, the plan is not cut.
It is also worth saying that if you do not call the toJSON method, then the plan is not cut:
// Plan is not being cut.
val textPlan = myPersistDf.queryExecution.optimizedPlan.toString
I made a small test project to make sure of this:
https://github.com/MinorityMeaning/CutPlanDataFrame
Please help me figure it out.
I need to get the full plan in json format.
UPD(1):
Now I'm trying to convert each node to json separately. Now it doesn't work perfectly, but I think we need to go in this direction.
val jsonPlan = s"[${getJson(result_df.queryExecution.optimizedPlan).mkString(",")}]"
def getJson(lp: TreeNode[_]): Seq[String] = {
val children = (lp.innerChildren ++ lp.children.map(c => c.asInstanceOf[TreeNode[_]])).distinct
JsonMethods.compact(JsonMethods.render(JsonMethods.parse(lp.toJSON)(0))) +:
getJson(t.asInstanceOf[TreeNode[_]])))
children.flatMap(t => getJson(t))
}
UPD(2):
OK, here's how I finally solved this problem.
I downloaded spark 3.0.1 from github. Then replaced the TreeNode class in this project with a file from spark 3.1.2. Recompiled the project.
As a result, I received a package spark-catalyst_2.12-3.0.1.jar
Which replaced the existing original packaging.
There is no option to switch to another version of spark. I have not found any other solutions to the problem.
Thank you guys for prompting. Your advice helped.
You can cherry pick below 2 commits into spark 3.0.1 to fix this issue.
* 1603775934 - [SPARK-35411][SQL][FOLLOWUP] Handle Currying Product while serializing TreeNode to JSON (8 months ago) <Tengfei Huang>
* 9804f07c17 - [SPARK-35411][SQL] Add essential information while serializing TreeNode to json (9 months ago) <Tengfei Huang>
Well, you already kinda have your answer.
TLDR: Upgrade.
You could, could, look into the github repo at the logical plan code, and look for difference in how logical plans are calculated between. 3.0.1 and 3.1.2. (Or look at the code for persist and see how it's changed.) You could then back port a patch to 3.0.1. But you'd still need to build a new version of spark and then deploy it so that the plan returned. But if you are doing all that work why not upgrade to 3.1.2 if you know it works? (Or some later version of Spark?)
(You must have some dependency on a sub-component that only compatible with 3.0.1?)
Is there a way to make the logged user (on superset) to make the queries on impala?
I tried to enable the "Impersonate the logged on user" option on Databases but with no success because all the queries run on impala with superset user.
I'm trying to achieve the same! This will not completely answer this question since it does not still work but I want to share my research in order to maybe help another soul that is trying to use this instrument outside very basic use cases.
I went deep in the code and I found out that impersonation is not implemented for Impala. So you cannot achieve this from the UI. I found out this PR https://github.com/apache/superset/pull/4699 that for whatever reason was never merged into the codebase and tried to copy&paste code in my Superset version (1.1.0) but it didn't work. Adding some logs I can see that the configuration with the impersonation is updated, but then the actual Impala query is with the user I used to start the process.
As you can imagine, I am a complete noob at this. However I found out that the impersonation thing happens when you create a cursor and there is a constructor parameter in which you can pass the impersonation configuration.
I managed to correctly (at least to my understanding) implement impersonation for the SQL lab part.
In the sql_lab.py class you have to add in the execute_sql_statements method the following lines
with closing(engine.raw_connection()) as conn:
# closing the connection closes the cursor as well
cursor = conn.cursor(**database.cursor_kwargs)
where cursor_kwargs is defined in db_engine_specs/impala.py as the following
#classmethod
def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
logger.info(
'Passing Impala execution_options.cursor_configuration for impersonation')
return {'execution_options': {
'cursor_configuration': {'impala.doas.user': username}}}
#classmethod
def get_cursor_configuration_for_impersonation(cls, uri, impersonate_user,
username):
logger.debug('Passing Impala cursor configuration for impersonation')
return {'configuration': {'impala.doas.user': username}}
Finally, in models/core.py you have to add the following bit in the get_sqla_engine def
params = extra.get("engine_params", {}) # that was already there just for you to find out the line
self.cursor_kwargs = self.db_engine_spec.get_cursor_configuration_for_impersonation(
str(url), self.impersonate_user, effective_username) # this is the line I added
...
params.update(self.get_encrypted_extra()) # already there
#new stuff
configuration = {}
configuration.update(
self.db_engine_spec.get_configuration_for_impersonation(
str(url),
self.impersonate_user,
effective_username))
if configuration:
params.update(configuration)
As you can see I just shamelessy pasted the code from the PR. However this kind of works only for the SQL lab as I already said. For the dashboards there is an entirely different way of querying Impala that I did not still find out.
This means that queries for the dashboards are handled in a different way and there isn't something like this
with closing(engine.raw_connection()) as conn:
# closing the connection closes the cursor as well
cursor = conn.cursor(**database.cursor_kwargs)
My gut (and debugging) feeling is that you need to first understand the sqlalchemy part and extend a new ImpalaEngine class that uses a custom cursor with the impersonation conf. Or something like that, however it is not simple (if we want to call this simple) as the sql_lab part. So, the trick is to find out where the query is executed and create a cursor with the impersonation configuration. Easy, isnt'it ?
I hope that this could shed some light to you and the others that have this issue. Let me know if you did find out another way to solve this issue, or if this comment was useful.
Update: something really useful
A colleague of mine succesfully implemented impersonation with impala without touching any superset related, but instead working directly with the impyla lib. A PR was open with the code to change. You can apply the patch directly in the impyla src used by superset. You have to edit both dbapi.py and hiveserver2.py.
As a reminder: we are still testing this and we do not know if it works with different accounts using the same superset instance.
I'm working on a quite simple ruby on rails website using MongoDB as the database. My idea was to get this website working with Mongoid such that I can display certain contents from the already existing mongodb collection. I've checked the Internet for tutorials about how to use Mongoid, the problem is all of them are about how to create your Mongodb with rails rather than using an existing one. Could anyone tell how to do what I want? Thanks a lot.
Until you define models for your collections,
you will have to drop down to the Moped driver level to examine existing collections.
Here's a link to the Moped documentation - http://mongoid.org/en/moped/docs/driver.html
And here are some hints to get you started.
console-input.rb
session = Mongoid.default_session
collection = session['test_collection']
collection.insert({name: 'George Washington'})
session.collection_names
collection.find.first
$ rails c < console-input.rb
Loading development environment (Rails 3.2.14)
Switch to inspect mode.
session = Mongoid.default_session
<Moped::Session seeds=["localhost:27017"] database=sandbox_mongoid3_development>
collection = session['test_collection']
#<Moped::Collection:0x007ff2ceb73c20 #database=#<Moped::Database:0x007ff2ceb68b40 #session=<Moped::Session seeds=["localhost:27017"] database=sandbox_mongoid3_development>, #name="sandbox_mongoid3_development">, #name="test_collection">
collection.insert({name: 'George Washington'})
nil
session.collection_names
["test_collection"]
collection.find.first
{"_id"=>"528bdc60e277b0dd1681771a", "name"=>"George Washington"}
In my application, in the homepage action, I call a specific web service that returns JSON.
parsed = JSON.parse(open("http://myservice").read)
#history = parsed['DATA']
This data will not change more than once per 60 seconds and does not change on a per-visitor basis, so i would like to, ideally, cache the #history variable itself (since the parsing will not result in a new result) and auto invalidate it if it is more than a minute old.
I'm unsure of the best way to do this. The default Rails caching methods all seem to be more oriented towards content that needs to be manually expired. I'm sure there is a quick and easy method to do this, I just don't know what it is!
You can use the built in Rails cache for this:
#history = Rails.cache.fetch('parsed_myservice_data', :expires_in => 1.minute) do
JSON.parse connector.get_response("http://myservice")
end
One problem with this approach is when the rebuilding of the data to be cached takes
quite a long time. If you get many client requests during this time, each of them will
get a cache miss and call your block, resulting in lots of duplicated effort, not to mention slow response times.
EDIT: In Rails 3.x you can pass the option :race_condition_ttl to the fetch method to avoid this problem. Read more about it here.
A good solution to this in previous versions of Rails is to setup a background/cron job to be run at regular intervals that will fetch and parse the data and update the cache.
In your controller or model:
#history = Rails.cache.fetch('parsed_myservice_data') do
JSON.parse connector.get_response("http://myservice")
end
In your background/cron job:
Rails.cache.write('parsed_myservice_data',
JSON.parse connector.get_response("http://myservice"))
This way, your client requests will always get fresh cached data (except for the first
request if the background/cron job hasn't been run yet.)
I don't know of an easy railsy way of doing this. You might want to look into using redis. Redis lets you set expiration times on the data you store in it. Depending on which redis gem you use it'd look something like this:
#history = $redis.get('history')
if not #history
#history = JSON.parse(open("http://myservice").read)['DATA']
$redis.set('history', #history)
$redis.expire('history', 60)
end
Because there's only one redis service this will work for all your rails processes.
We had a similar requirement and we ended up using Squid as a forward proxy for all the webservice calls from the rails server. Squid was configured to have a cache-expiry time of 60 seconds.
http_connection_factory.rb:
class HttpConnectionFactory
def self.connection
AppConfig.use_forward_proxy ? Net::HTTP::Proxy(AppConfig.forward_proxy_host, AppConfig.forward_proxy_port) : Net::HTTP
end
end
In your application's home page action, you can use the proxy instead of making the call directly.
connector = HttpConnectionFactory.connection
parsed = JSON.parse(connector.get_response("http://myservice"))
#history = parsed['DATA']
We had second thoughts about using Redis or Memcache. But, we had several service calls and wanted to avoid all the hassles of generating keys and sweeping them at appropriate times.
So, in our case, the forward proxy took care of all those nitty gritties. Please refer to Squid Wiki for the configuration parameters necessary.