userCache | one simple trick that can save you millions of database | Database library
kandi X-RAY | userCache Summary
kandi X-RAY | userCache Summary
userCache builds upon three simple premises:. Thus, instead of querying the database every time that Meteor.user() is ran, we could first see if it's sufficient to retrieve it from the server-side cache (also known as MergeBox). Since MergeBox is fast and real-time (in fact, it gets the data before the client), the risk of stale data may be insignificant.
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QUESTION
I follow the official tutotial from microsoft: https://docs.microsoft.com/en-us/azure/synapse-analytics/machine-learning/tutorial-score-model-predict-spark-pool
But when I execute:
...ANSWER
Answered 2022-Mar-29 at 07:54(UPDATE:29/3/2022): You will experiencing this error message if you model does not contains all the required files in the ML model.
As per the repro, I had created two ML models named:
sklearn_regression_model: Which contains only
sklearn_regression_model.pklfile.
When I predict for MLFLOW packaged model named
sklearn_regression_model, getting same error as shown above:
linear_regression: Which contains the below files:
When I predict for MLFLOW packaged model named
linear_regression, it works as excepted.
It should be AML_MODEL_URI = "" #In URI ":x" =>
Rossman_Sales:2
Before running this script, update it with the URI for ADLS Gen2 data file along with model output return data type and ADLS/AML URI for the model file.
QUESTION
I was using pyspark on AWS EMR (4 r5.xlarge as 4 workers, each has one executor and 4 cores), and I got AttributeError: Can't get attribute 'new_block' on . Below is a snippet of the code that threw this error:
...ANSWER
Answered 2021-Aug-26 at 14:53I had the same error using pandas 1.3.2 in the server while 1.2 in my client. Downgrading pandas to 1.2 solved the problem.
QUESTION
I just downloaded activiti-app from github.com/Activiti/Activiti/releases/download/activiti-6.0.0/… and deployed in tomcat9, but I have this errors when init the app:
ANSWER
Answered 2021-Dec-16 at 09:41Your title says you are using Java 9. With Activiti 6 you will have to use JDK 1.8 (Java 8).
QUESTION
I am trying to test a class like
...ANSWER
Answered 2021-Dec-08 at 20:22It looks like you would like to run the full-fledged spring boot test with all the beans but in the application context you would like to "mock" some real beans and provide your own (mock-y) implementation.
If so, the usage of @Mock is wrong here. @Mock has nothing to do with spring, its a purely mockito's thing. It indeed can create a mock for you but it won't "substitute" the real implemenation with this mock implementation in the spring boot's application context.
For that purpose use @MockBean annotation instead. This is something from the spring "universe" that indeed creates a mockito driven mock under the hood, but substitutes the regular bean in the application context (or even just adds this mock implementation to the application context if the real bean doesn't even exist).
Another thing to consider is how do you get the TotalCalculator bean (although you don't directly ask this in the question).
The TotalCalculator by itself is probably a spring been that spring boot creates for you, so if you want to run a "full fledged" test you should take the instance of this bean from the application context, rather than creating an instance by yourself. Use annotation @Autowired for that purpose:
QUESTION
All,
We have a Apache Spark v3.12 + Yarn on AKS (SQLServer 2019 BDC). We ran a refactored python code to Pyspark which resulted in the error below:
Application application_1635264473597_0181 failed 1 times (global limit =2; local limit is =1) due to AM Container for appattempt_1635264473597_0181_000001 exited with exitCode: -104
Failing this attempt.Diagnostics: [2021-11-12 15:00:16.915]Container [pid=12990,containerID=container_1635264473597_0181_01_000001] is running 7282688B beyond the 'PHYSICAL' memory limit. Current usage: 2.0 GB of 2 GB physical memory used; 4.9 GB of 4.2 GB virtual memory used. Killing container.
Dump of the process-tree for container_1635264473597_0181_01_000001 :
|- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
|- 13073 12999 12990 12990 (python3) 7333 112 1516236800 235753 /opt/bin/python3 /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp/3677222184783620782
|- 12999 12990 12990 12990 (java) 6266 586 3728748544 289538 /opt/mssql/lib/zulu-jre-8/bin/java -server -XX:ActiveProcessorCount=1 -Xmx1664m -Djava.io.tmpdir=/var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp -Dspark.yarn.app.container.log.dir=/var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class org.apache.livy.rsc.driver.RSCDriverBootstrapper --properties-file /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_conf.properties --dist-cache-conf /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_dist_cache.properties
|- 12990 12987 12990 12990 (bash) 0 0 4304896 775 /bin/bash -c /opt/mssql/lib/zulu-jre-8/bin/java -server -XX:ActiveProcessorCount=1 -Xmx1664m -Djava.io.tmpdir=/var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/tmp -Dspark.yarn.app.container.log.dir=/var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001 org.apache.spark.deploy.yarn.ApplicationMaster --class 'org.apache.livy.rsc.driver.RSCDriverBootstrapper' --properties-file /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_conf.properties --dist-cache-conf /var/opt/hadoop/temp/nm-local-dir/usercache/grajee/appcache/application_1635264473597_0181/container_1635264473597_0181_01_000001/spark_conf/spark_dist_cache.properties 1> /var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001/stdout 2> /var/log/yarnuser/userlogs/application_1635264473597_0181/container_1635264473597_0181_01_000001/stderr
[2021-11-12 15:00:16.921]Container killed on request. Exit code is 143
[2021-11-12 15:00:16.940]Container exited with a non-zero exit code 143.
For more detailed output, check the application tracking page: https://sparkhead-0.mssql-cluster.everestre.net:8090/cluster/app/application_1635264473597_0181 Then click on links to logs of each attempt.
. Failing the application.
The default setting is as below and there are no runtime settings:
"settings": {
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.driver.memory": "1664m",
"spark-defaults-conf.spark.driver.memoryOverhead": "384",
"spark-defaults-conf.spark.executor.instances": "1",
"spark-defaults-conf.spark.executor.cores": "2",
"spark-defaults-conf.spark.executor.memory": "3712m",
"spark-defaults-conf.spark.executor.memoryOverhead": "384",
"yarn-site.yarn.nodemanager.resource.memory-mb": "12288",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "12288",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.34".
}
Is the AM Container mentioned the Application Master Container or Application Manager (of YARN). If this is the case, then in a Cluster Mode setting, the Driver and the Application Master run in the same Container?
What runtime parameter do I change to make the Pyspark code successfully.
Thanks,
grajee
ANSWER
Answered 2021-Nov-19 at 13:36Likely you don't change any settings 143 could mean a lot of things, including you ran out of memory. To test if you ran out of memory. I'd reduce the amount of data you are using and see if you code starts to work. If it does it's likely you ran out of memory and should consider refactoring your code. In general I suggest trying code changes first before making spark config changes.
For an understanding of how spark driver works on yarn, here's a reasonable explanation: https://sujithjay.com/spark/with-yarn
QUESTION
I am trying to run "onto_electra_base_uncased" model on some data stored in hive table, I ran count() on df before saving the data into hive table and got this exception.
Spark Shell launch configurations:
...ANSWER
Answered 2021-Nov-18 at 09:23The solution to this issue is use kryo serialization, the default spark-shell or spark-submit invocation is using java serialization, the Annotate class in spark-nlp is implemented to use Kryo Serialization hence same should be used for running any spark-nlp jobs
QUESTION
I'm launching AWS EMR jobs via Airflow which rely on saving the data onto a PostgreSQL database. Unfortunately, as far as I can tell, the connector is not available by default in EMR hence the error:
...ANSWER
Answered 2021-Oct-14 at 18:55Im not sure how the emr is being provisioned but below is how you would do it.
First upload the postgres jdbc jar to an s3 location. THen refer to that when you provision the cluster.
If you are provisioning via Cloudformation then below is what you will need to do
QUESTION
I am working on a spring data project and tying to integrate Ignite cache with it. I was using already using PagingAndSortingRepository
...ANSWER
Answered 2021-Oct-10 at 09:13According to your error UserCacheRepository inherits IgniteRepository.deleteAllById(Iterable ids) and CrudRepository.deleteAllById(Iterable ids), but erasures for Iterable and Iterable would be the same (just Iterable), so we get into situation when class has two methods with absolutely same signatures and this lead to name clash error.
Root cause for this error is that IgniteRepository originally was written when CrudRepository didn't have deleteAllById() method, meanwhile CrudRepository that comes with modern versions of spring-boot-starter-data-jpa has this method.
You may try to use older version for spring data if it possible for rest of your application.
Also you mey try to explicitly override deleteAllById(Iterable iterable) method, but I'm not sure if it helps.
The best option is to update apache-ignite-extensions to work with latest spring data, so you can create a Jira ticket in Apache Ignite project for this.
QUESTION
I have the pyspark dataframe df below. It has the schema shown below. I've also supplied some sample data, and the desired out put I'm looking for. The problem I'm having is the attributes column has values that are dictionaries. I would like to create new columns for each key in the dictionaries but the values in the attribute column are string. So I'm having trouble using explode or from_json.
I made an attempt based on another SO post using explode, the code I ran and the error are below the example data and desired output.
Also I don't know what all the keys in the dict might be, since different records have different length dicts.
does anyone have a suggestion how to do this? I was thinking of converting it to pandas and trying to solve it that way, but I'm hoping there's a better/faster pyspark solution.
...ANSWER
Answered 2021-Oct-05 at 01:58Try using from_json function with the corresponding schema to parse the json string
QUESTION
I'm trying to do a search in Splunk in which I'm trying to narrow it down to a unique substring.
An example of my query so far would be:
...ANSWER
Answered 2021-Aug-05 at 11:12That calls for the dedup command, which removes duplicates from the search results. First, however, we need to extract the user name into a field. We'll do that using rex.
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