I'm working on a project that involves reading data from RDBMS using JDBC and I succeeded reading the data. This is something I will be doing fairly constantly, weekly. So I've been trying to come up with a way to ensure that after the initial read, subsequent ones should only pull updated records instead of pulling the entire data from the table.
I can do this with sqoop incremental import by specifying the three parameters (--check-column, --incremental last-modified/append and --last-value). However, I dont want to use sqoop for this. Is there a way I can replicate same in Spark with Scala?
Secondly, some of the tables do not have unique column which can be used as partitionColumn, so I thought of using a row-number function to add a unique column to these table and then get the MIN and MAX of the unique column as lowerBound and upperBound respectively. My challenge now is how to dynamically parse these values into the read statement like below:
val queryNum = "select a1.*, row_number() over (order by sales) as row_nums from (select * from schema.table) a1"
val df = spark.read.format("jdbc").
option("driver", driver).
option("url",url ).
option("partitionColumn",row_nums).
option("lowerBound", min(row_nums)).
option("upperBound", max(row_nums)).
option("numPartitions", some value).
option("fetchsize",some value).
option("dbtable", queryNum).
option("user", user).
option("password",password).
load()
I know the above code is not right and might be missing a whole lot of processes but I guess it'll give a general overview of what I'm trying to achieve here.
It's surprisingly complicated to handle incremental JDBC reads in Spark. IMHO, it severely limits the ease of building many applications and may not be worth your trouble if Sqoop is doing the job.
However, it is doable. See this thread for an example using the dbtable option:
Apache Spark selects all rows
To keep this job idempotent, you'll need to read in the max row of your prior output either directly from loading all data files or via a log file that you write out each time. If your data files are massive you may need to use the log file, if smaller you could potentially load.
Related
I'm very new to the ETL world and I wish to implement Incremental Data Loading with Cassandra 3.7 and Spark. I'm aware that later versions of Cassandra do support CDC, but I can only use Cassandra 3.7. Is there a method through which I can track the changed records only and use spark to load them, thereby performing incremental data loading?
If it can't be done on the cassandra end, any other suggestions are also welcome on the Spark side :)
It's quite a broad topic, and efficient solution will depend on the amount of data in your tables, table structure, how data is inserted/updated, etc. Also, specific solution may depend on the version of Spark available. One downside of Spark-only method is you can't easily detect deletes of the data, without having a complete copy of previous state, so you can generate a diff between 2 states.
In all cases you'll need to perform full table scan to find changed entries, but if your table is organized specifically for this task, you can avoid reading of all data. For example, if you have a table with following structure:
create table test.tbl (
pk int,
ts timestamp,
v1 ...,
v2 ...,
primary key(pk, ts));
then if you do following query:
import org.apache.spark.sql.cassandra._
val data = spark.read.cassandraFormat("tbl", "test").load()
val filtered = data.filter("""ts >= cast('2019-03-10T14:41:34.373+0000' as timestamp)
AND ts <= cast('2019-03-10T19:01:56.316+0000' as timestamp)""")
then Spark Cassandra Connector will push this query down to the Cassandra, and will read only data where ts is in the given time range - you can check this by executing filtered.explain and checking that both time filters are marked with * symbol.
Another way to detect changes is to retrieve the write time from Cassandra, and filter out the changes based on that information. Fetching of writetime is supported in RDD API for all recent versions of SCC, and is supported in the Dataframe API since release of SCC 2.5.0 (requires at least Spark 2.4, although may work with 2.3 as well). After fetching this information, you can apply filters on the data & extract changes. But you need to keep in mind several things:
there is no way to detect deletes using this method
write time information exists only for regular & static columns, but not for columns of primary key
each column may have its own write time value, in case if there was a partial update of the row after insertion
in most versions of Cassandra, call of writetime function will generate error when it's done for collection column (list/map/set), and will/may return null for column with user-defined type
P.S. Even if you had CDC enabled, it's not a trivial task to use it correctly:
you need to de-duplicate changes - you have RF copies of the changes
some changes could be lost, for example, when node was down, and then propagated later, via hints or repairs
TTL isn't easy to handle
...
For CDC you may look for presentations from 2019th DataStax Accelerate conference - there were several talks on that topic.
I am working with graphframes, pyspark, and hive to work with graph data. As I process data I will be building a graph and eventually will be persisting this data into a Hive table, where I will not update it ever again.
Subsequent runs may have relationships to nodes from previous runs, so I will want to ensure I don't duplicate data.
For example, run #1 might find nodes: A, B, C. Run #2 might re-find node A, and also find new nodes X, Y, Z. I do not want A to appear twice in my table.
I am looking for the best way to handle this and would like to address the following issues:
I will need to track the status of the node as I process metadata associated with it. I will only want to persist the node's data to Hive after I have finished this processing.
I want to ensure that I don't create duplicate data when I encounter the same node (e.g. when I re-find A node above, I don't want to insert another row into Hive)
I am currently tinkering with the best way to do this. I know hive supports ACID transactions now, but it does not appear as though pyspark currently supports CRUD type operations. So here is what I'm planning on:
On each run, create a dataframe to store the nodes I have found.
When a new node is found: Check if the node already exists in Hive (e.g. sqlContext.sql("SELECT * FROM existingTable WHERE name="<NAME>"). If it does not exist update the dataframe with x = vertices.withColumn("name", F.when(F.col("id")=="a", "<THE-NEW-NAME>").otherwise(F.col("name"))) to add it to our Dataframe.
Once all the nodes have finished processing, create a temporary view: x.createOrReplaceTempView("myTmpView")
Finally, insert data from my temporary view into an existing table with sqlContext.sql("INSERT INTO TABLE existingTable SELECT * FROM myTmpView")
I think this will work, but it seems extremely hacky. I'm not sure if this is a function of my lack of understanding of Hive/Spark, or if this is just the nature of the tech stack. Is there a better way to do this? Is there a performance cost to handling it in this way?
In deltalake api, upserts(Merge) are supported using scala and also python. Which is exactly you are trying to implement.
https://docs.delta.io/latest/delta-update.html#merge-examples
Here is an alternate solution
Have a column updated_time timestamp in your table
union prev_run_results and current_run_results
group by 'node', select the latest timestamp
save the results
Well the the title of the questions says it all. I have a requirement which requires getting row keys corresponding to top X (say top 10) values in certain column. Thus, I need to sort hbase rows by the desired column values. I don't understand how should I do this or even is doable or not. It seems that hbase does not cater to this very well. Also it does not allow any such functionality out of the box.
Q1. Can I use hbase-spark connector, load whole hbase data in spark rdd and then perform sorting in it? Will this be fast? How the connector and spark will handle it? Will it fetch whole data on single node or multiple nodes and sort in distributed manner?
Q2. Also is there any better way to do this?
Q3. Is it undoable in hbase at all? and should I opt for different framework/technology altogether?
A3. If you need to sort your data by some column (not row-key), you get no benefit from using HBase. It'll be the same as reading raw files from hive/hdfs and sort, but slower.
A1. Sure you can use SHC or any other spark-hbase library for that matter, but A3 still holds. It will load the entire data on every region server as Spark RDD, only to shuffle it across your entire cluster.
A2. As any other programming/architecture issue, there are many possible solutions depending on your resources and requirements.
Will spark load all the data on single node and do sorting on single node or will it perform sorting on different nodes?
It depends on two factors:
How many regions your table has: This determines the parallelism degree (number of partitions) for reading from your table.
spark.sql.shuffle.partitions configuration value: After loading the data from the table, this value determines the parallelism degree for the sorting stage.
is there any better [library] than the SHC?
As for today there are multiple libraries for integrating Spark with HBase, each has its own pros and cons, and TMO none of them is fully mature or gives full coverage (compared Spark-Hive integration, for example). To get the best from Spark over HBase you should have a very good understanding of your use case and select the most suitable library.
Q2. Also is there any better way to do this?
If re-designing your HBase table is an option with this specific column value as part of the rowkey, this would allow fast access to these values as HBase is optimised for rowkey filters and not column filters.
You could then create a rowkey concatenation of the existing_rowkey + this_col_value. Querying it then with a Row Filter would have better performance results.
I have an exchangeRates table that gets updated in batch once per week. This is to be used by other batch and streaming jobs, across different clusters - thus I want to save this as a persistent, shared table for all to jobs share.
allExchangeRatesDF.write.saveAsTable("exchangeRates")
How best then (for the batch job that manages this data) to gracefully update the table contents (actually overwrite it completely) - considering the various spark job as consumers of it and particularily giving its use in some 24/7 structured streaming streams?
Ive checked the APIs, maybe I am missing something obvious! Very likely.
Thanks!
I think you expect some kind of transaction support from Spark so when there's saveAsTable in progress Spark would hold all writes until the update/reset has finished.
I think that the best way to deal with the requirement is to append new records (using insertInto) with the batch id that would denote the rows that belong to a "new table".
insertInto(tableName: String): Unit Inserts the content of the DataFrame to the specified table. It requires that the schema of the DataFrame is the same as the schema of the table.
You'd then use the batch id to deal with the rows as if they were the only rows in the dataset.
I have data that I would like to do a lot of analytic queries on and I'm trying to figure out if there is a mechanism I can use to store it so that Spark can efficiently do joins on it. I have a solution using RedShift, but would ideally prefer to have something that is based on files in S3 instead of having a whole RedShift cluster up 24/7.
Introduction to the data
This is a simplified example. We have 2 initial CSV files.
Person records
Event records
The two tables are linked via the person_id field. person_id is unique in the Person table. Events have a many-to-one relationship with person.
The goal
I'd like to understand how to set up the data so I can efficiently perform the following query. I will need to perform many queries like this (all queries are evaluated on a per person basis):
The query is to produce a data frame with 4 columns, and 1 row for every person.
person_id - person_id for each person in the data set
age - "age" field from the person record
cost - The sum of the "cost" field for all event records for that person where "date" is during the month of 6/2013
All current solutions I have with Spark to this problem involve reshuffling all the data, which ends up making the process slow for large amounts (hundreds of millions of people). I am happy with a solution that requires me to reshuffle the data and write it to a different format once if that can then speed up later queries.
The solution using RedShift
I can accomplish this solution using RedShift in a fairly straightforward way:
Each both files are loaded in as RedShift tables, with DISTKEY person_id, SORTKEY person_id. This distributes the data so that all the data for a person is on a single node. The following query will produce the desired data frame:
select person_id, age, e.cost from person
left join (select person_id, sum(cost) as cost from events
where date between '2013-06-01' and '2013-06-30'
group by person_id) as e using (person_id)
The solution using Spark/Parquet
I have thought of several potential ways to handle this in Spark, but none accomplishes what I need. My ideas and the issues are listed below:
Spark Dataset write 'bucketBy' - Read the CSV files and then rewrite them out as parquet files using "bucketBy". Queries on these parquet files could then be very fast. This would produce a data setup similar to RedShift, but parquet files don't support bucketBy.
Spark parquet partitioning - Parquet does support partitioning. Because parquet creates a separate set of files for each partition key, you have to create a computed column to partition on and use a hash of person_id to create the partitionKey. However, when you later join these tables in spark based on "partition_key" and "person_id", the query plan still does a full hash partition. So this approach is no better than just reading the CSVs and shuffling every time.
Stored in some other data format besides parquet - I am open to this, but don't know of another data source that will work.
Using a compound record format - Parquet supports hierarchical data formats, so can prejoin both tables into a hierarchical record (where a person record has an "events" field which is an array of struct elements) and then do processing on that. When you have a hierarchical record, there are two approaches that to processing it:
** Use explode to create separate records ** - Using this approach you explode array fields into full rows, then use standard data frame operations to do analytics, and then join them back to the main table. Unfortunately, I've been unable to get this approach to efficiently compile queries.
** Use UDFs to perform operations on subrecords ** - This preserves the structure and executes without shuffles, but is an awkward and verbose way to program. Also, it requires lots of UDFs which aren't great for performance (although they beat large scale shuffling of data).
For my use cases, Spark has advantages over RedShift which aren't obvious in this simple example, so I'd prefer to do this with Spark. Please let me know if I am missing something and there is a good approach to this.
Edited per comment.
Assumptions:
Using parquet
Here's what I would try:
val eventAgg = spark.sql("""select person_id, sum(cost) as cost
from events
where date between '2013-06-01' and '2013-06-30'
group by person_id""")
eventAgg.cache.count
val personDF = spark.sql("""SELECT person_id, age from person""")
personDF.cache.count // cache is less important here, so feel free to omit
eventAgg.join(personDF, "person_id", "left")
I just did this with some of my data and here's how it went (9
node/140 vCPUs cluster, ~600GB RAM):
27,000,000,000 "events" (aggregated to 14,331,487 "people")
64,000,000 "people" (~20 columns)
aggregated events building and caching took ~3 min
people caching took ~30 seconds (pulling from network, not parquet)
left joining took several seconds
Not caching the "people" led to the join taking a few seconds longer. Then forcing spark to broadcast the couple hundred MB aggregated events made the join take under 1 second.