DateTimeUtils | Android library that regroup bunch of dateTime utilities | Date Time Utils library
kandi X-RAY | DateTimeUtils Summary
kandi X-RAY | DateTimeUtils Summary
This library is a package of functions that let you manipulate objects and or java date string. it combine the most common functions used when managing dates under android, such as converting a mysql /sqlLite date to a Date object and vis-versa etc. This library is available under the MIT License.
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Top functions reviewed by kandi - BETA
- Initialize the activity
- Convert a date string to a Java Date Object
- Gets the previous week date
- Gets the next month from a date
- Get the previous month date from the given date
- Get date or date format pattern
- Checks if a given string represents a date time
- Enable debugging
- Set the time zone
- Converts milliseconds to human readable time
- Convert milliseconds to a human readable time string
DateTimeUtils Key Features
DateTimeUtils Examples and Code Snippets
Community Discussions
Trending Discussions on DateTimeUtils
QUESTION
I want to work with a parquet table with certain types of fields:
name_process: String id_session: String time_write: String key: String value: String
"id_session" is a id for SparkSession.
The table is partitioned by the "name_process" column
For example:
name_process id_session time_write key value OtherClass sess000001 1639950466114000 schema0.table0.csv Success OtherClass sess000002 1639950466214000 schema1.table1.csv Success OtherClass sess000003 1639950466309000 schema0.table0.csv Success OtherClass sess000003 1639950466310000 schema1.table1.csv Failure OtherClass sess000003 1639950466311000 schema2.table2.csv Success OtherClass sess000003 1639950466312000 schema3.table3.csv Success ExternalClass sess000004 1639950466413000 schema0.table0.csv SuccessAll values for the "key" column are unique only within one spark session (the "id_session" column). This happens because I work with the same files (csv) every time I start a spark session. I plan to send these files to the server. Both the time of sending and the response from the server will be recorded in the "time_write" and "value" columns. That is, I want to see the latest sending statuses for all csv files.
This is a log for entries that I will interact with. To interact with this log, I want to implement several methods:
All getters methods will return filtered DateFrames with all columns. That is, the result remains 5 columns. I'm still having difficulties with API Spark. It will take some time until I learn how to perform beautiful operations on DataFrames. Here's what my result is:
...ANSWER
Answered 2021-Dec-30 at 14:33It's been a long time. And i leave my decision this.
QUESTION
I'm trying to make year and month columns from a column named logtimestamp (of type TimeStampType) in spark. The data source is cassandra. I am using sparkshell to perform these steps, here is the code I have written -
...ANSWER
Answered 2021-Nov-03 at 11:14Turns out one of the cassandra table had a timestamp value that was greater than the highest value allowed by spark but not large enough to overflow in cassandra. The timestamp had been manually edited to get around the upserting that is done by default in cassandra, but this led to some large values being formed during development. Ran a python script to find this out.
QUESTION
Now I am executing a test case but am getting a NoSuchElementException despite not using that element in my test case. Here is my implementation.
This is my page layer
...ANSWER
Answered 2021-Oct-14 at 05:26You should not declare like this,
QUESTION
We have noticed excessive logging from the TrackingEventProcessor class when scaling up the micro-service to 2 replicas:
Our Axon setup:
- axon version 3.4.3
- spring-boot version 2.1.6.RELEASE
- an in-house couchbase implementation as the
TokenStore, i.e.CouchBaseTokenStore - PostgreSQL v11.7 as event store
- Segment count for each tracking event processor = 1
- Tracking Event Processors are configured
forSingleThreadedProcessing
We are seeing the following messages a lot:
...ANSWER
Answered 2021-Apr-15 at 19:54I managed to fix the problem with the help of Allard (see comments on question). The fix was to also persist the token after it has been claimed in the fetch() method. We also started making use of the replace() method supplied by the Couchbase SDK instead of the upsert() method, to better harness the CAS (Compare-and-Swap) optimistic concurency:
QUESTION
I got the error bellow when I ran an application Java Web using Cassandra 3.11.9 and Spark 3.0.1.
My question is why did it happen only after deploy the application? In the development environment it did not occur.
2021-03-24 08:50:41.150 INFO 19613 --- [uler-event-loop] org.apache.spark.scheduler.DAGScheduler : ShuffleMapStage 0 (collectAsList at FalhaService.java:60) failed in 7.513 s due to Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0) (GDBHML08 executor driver): java.lang.ArithmeticException: integer overflow at java.lang.Math.toIntExact(Math.java:1011) at org.apache.spark.sql.catalyst.util.DateTimeUtils$.fromJavaDate(DateTimeUtils.scala:90) at org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:306) at org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:305) at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:107) at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:252) at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:242) at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:107) at org.apache.spark.sql.catalyst.CatalystTypeConverters$.$anonfun$createToCatalystConverter$2(CatalystTypeConverters.scala:426) at com.datastax.spark.connector.datasource.UnsafeRowReader.read(UnsafeRowReaderFactory.scala:34) at com.datastax.spark.connector.datasource.UnsafeRowReader.read(UnsafeRowReaderFactory.scala:21) at com.datastax.spark.connector.datasource.CassandraPartitionReaderBase.$anonfun$getIterator$2(CassandraScanPartitionReaderFactory.scala:110) at scala.collection.Iterator$$anon$10.next(Iterator.scala:461) at scala.collection.Iterator$$anon$11.next(Iterator.scala:496) at com.datastax.spark.connector.datasource.CassandraPartitionReaderBase.next(CassandraScanPartitionReaderFactory.scala:66) at org.apache.spark.sql.execution.datasources.v2.PartitionIterator.hasNext(DataSourceRDD.scala:79) at org.apache.spark.sql.execution.datasources.v2.MetricsIterator.hasNext(DataSourceRDD.scala:112) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithKeys_0$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:755) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:132) at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) at org.apache.spark.scheduler.Task.run(Task.scala:131) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:497) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1439) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:500) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
Driver stacktrace: 2021-03-24 08:50:41.189 INFO 19613 --- [nio-8080-exec-2] org.apache.spark.scheduler.DAGScheduler : Job 0 failed: collectAsList at FalhaService.java:60, took 8.160348 s
The line's code that it is in this error:
...ANSWER
Answered 2021-Mar-27 at 15:28It looks like that you have incorrect data in the database, some date field that is far into the future. If you look into the source code, you can see that it's converting first into milliseconds, and then converting into days, and this conversion overflows the integer. And this may explain why the code works in dev environment...
You may ask your administrator to check files for corrupted data, for example, using the nodetool scrub command.
P.S. are you sure that you're using Spark 3.0.1? The location of the function in the error is matching the Spark 3.1.1...
QUESTION
I've updated Vuetify from version 2.2.x to version 2.4.6 by running npm uninstall --save vuetify and then npm run install --save vuetify@latest. It's previously installed using vue add vuetify Now serving the project spews out these error messages
ANSWER
Answered 2021-Mar-14 at 16:32You should have a package.lock.json file. Delete that, and also delete the node_modules folder. Re-run npm install and try building again
QUESTION
I have a system in optaplanner which generates shifts for employees. When there are for e.x 6 employees and shift capacity is two, the solution is generated only for 2 employees until they reach the maximum working hours. How can I add a constraint so that the solution is generated with mixed employees.
Below are rules that are defined in optaplanner:
...ANSWER
Answered 2021-Feb-09 at 13:08You should add this configuration in solver config file
QUESTION
I am not able to convert timestamps in UTC to AKST timezone in Spark 3.0. The same works in Spark 2.4. All other conversions work (to EST, PST, MST etc).
Appreciate any inputs on how to fix this error.
Below command:
...ANSWER
Answered 2021-Jan-25 at 08:42Seems it can't understand AKST, but Spark 3 seems to understand America/Anchorage, which I suppose to have the timezone AKST:
QUESTION
I have an enum that looks like this
...ANSWER
Answered 2020-Sep-03 at 13:10You can use the class Calendar to resolve your problem like that :
QUESTION
I have a case class generated from a .proto file through scalapb, which has a few fields with bcl.DateTime type. The case class definition is as follows:
...ANSWER
Answered 2020-Jul-11 at 02:32In order to use ScalaPB generated class with Spark, you need to add a library dependency on sparksql-scalapb, and use ProtoSQL.createDataFrame() instead of spark.sqlContext.createDataFrame. The process is described here: https://scalapb.github.io/sparksql.html#using-sparksql-scalapb
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Vulnerabilities
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Install DateTimeUtils
You can use DateTimeUtils like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the DateTimeUtils component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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