partitioning

问题 So I am trying to implement a custom partitioner using Spark with Java , and I found a great example of how to do this online, but it is using Scala , and I cannot for the life of me figure out how it translates properly into Java so I can try to implement it. Can anyone help? Here is the example code I found for it in Scala : class DomainNamePartitioner(numParts: Int) extends Partitioner { override def numPartitions: Int = numParts override def getPartition(key: Any): Int = { val domain =

问题 I have been using the wonderful JQ library to parse and extract JSON data to facilitate re-importing. I am able to extract a range easily enough, but am unsure as to how you could loop through in a script and detect the end of the file, preferably in a bash or fish shell script. Given a JSON file that is wrapped in a "results" dictionary, how can I detect the end of the file? From testing, I can see that I will get an empty array nested in my desired structure, but how can you detect the end

问题 I've created a table with partition type day, and I have set time_partitioning_expiration to 1209600 seconds (14 days) from bq command line tool. I have verified if the settings are correct by running bq show on table and I can see "timePartitioning": { "expirationMs": "1209600000", "type": "DAY" }, "type": "TABLE" However there seems to be data in the partitions that I expected to have been deleted. SELECT count(*) as c, _partitiontime as pDate FROM [poc.reporting] group by pDate ; 1 373800

问题 I just try to find a way to get the locality of a RDD's partition in Spark. After calling RDD.repartition() or PairRDD.combineByKey() the returned RDD is partitioned. I'd like to know which worker instances the partitions are at (for examining the partition behaviour)?! Can someone give a clue? 回答1: An interesting question that I'm sure has not so much interesting answer :) First of all, applying transformations to your RDD has nothing to do with worker instances as they are separate

问题 sc = SparkContext("Local") rdd = sc.binaryFiles(Path to the binary file , minPartitions = 5).partitionBy(8) or sc = SparkContext("Local") rdd = sc.binaryFiles(Path to the binary file , minPartitions = 5).repartition(8) Using either of the above codes, I am trying to make 8 partitions in my RDD {wherein, I want the data to be distributed evenly on all the partitions} . When I am printing {rdd.getNumPartitions()} , the number of partitions shown are 8 only, but on Spark UI , I have observed

问题 Let there be an external table in Athena which points to a large amount of data stored in parquet format on s3. It contains a lot of columns and is partitioned on a field called 'timeid'. Now, there's another external table (small one) which maps timeid to date. When the smaller table is also partitioned on timeid and we join them on their partition id (timeid) and put date into where clause, only those specific records are scanned from large table which contain timeids corresponding to that

问题 I want to keep the last 45 days of log data in a MySQL table for statistical reporting purposes. Each day could be 20-30 million rows. I'm planning on creating a flat file and using load data infile to get the data in there each day. Ideally I'd like to have each day on it's own partition without having to write a script to create a partition every day. Is there a way in MySQL to just say each day gets it's own partition automatically? thanks 回答1: I would strongly suggest using Redis or

问题 Consider the following example: JavaPairRDD<String, Row> R = input.textFile("test").mapToPair(new PairFunction<String, String, Row>() { public Tuple2<String, Row> call(String arg0) throws Exception { String[] parts = arg0.split(" "); Row r = RowFactory.create(parts[0],parts[1]); return new Tuple2<String, Row>(r.get(0).toString(), r); }}).partitionBy(new HashPartitioner(20)); The code above creates an RDD named R which is partitioned in 20 pieces by hashing on the first column of a txt file

问题 I'm using static partition in hive to seggregate the data into subdirectories based on date field, I'll need 365 partitions/year for each table(total 14 tables) as I have daily loads into hive. Is there any limitation on number of static partitions that can be created in hive? Dynamic partition gives error if "hive.exec.max.dynamic.partitions.pernode" exceeds the specified thresold(100) in sqoop import I have 5 node HDP cluster out of which 3 are datanodes Will it hamper performace of cluster

问题 I'm trying to create a data import mechanism for a database that requires high availability to readers while serving irregular bulk loads of new data as they are scheduled. The new data involves just three tables with new datasets being added along with many new dataset items being referenced by them and a few dataset item metadata rows referencing those. Datasets may have tens of thousands of dataset items. The dataset items are heavily indexed on several combinations of columns with the