I'm using Dataflow SDK 2.X Java API ( Apache Beam SDK) to write data into mysql. I've created pipelines based on Apache Beam SDK documentation to write data into mysql using dataflow. It inserts single row at a time where as I need to implement bulk insert. I do not find any option in official documentation to enable bulk inset mode.
Wondering, if it's possible to set bulk insert mode in dataflow pipeline? If yes, please let me know what I need to change in below code.
.apply(JdbcIO.<KV<Integer, String>>write()
.withDataSourceConfiguration(JdbcIO.DataSourceConfiguration.create(
"com.mysql.jdbc.Driver", "jdbc:mysql://hostname:3306/mydb")
.withUsername("username")
.withPassword("password"))
.withStatement("insert into Person values(?, ?)")
.withPreparedStatementSetter(new JdbcIO.PreparedStatementSetter<KV<Integer, String>>() {
public void setParameters(KV<Integer, String> element, PreparedStatement query) {
query.setInt(1, kv.getKey());
query.setString(2, kv.getValue());
}
})
EDIT 2018-01-27:
It turns out that this issue is related to the DirectRunner. If you run the same pipeline using the DataflowRunner, you should get batches that are actually up to 1,000 records. The DirectRunner always creates bundles of size 1 after a grouping operation.
Original answer:
I've run into the same problem when writing to cloud databases using Apache Beam's JdbcIO. The problem is that while JdbcIO does support writing up to 1,000 records in one batch, in I have never actually seen it write more than 1 row at a time (I have to admit: This was always using the DirectRunner in a development environment).
I have therefore added a feature to JdbcIO where you can control the size of the batches yourself by grouping your data together and writing each group as one batch. Below is an example of how to use this feature based on the original WordCount example of Apache Beam.
p.apply("ReadLines", TextIO.read().from(options.getInputFile()))
// Count words in input file(s)
.apply(new CountWords())
// Format as text
.apply(MapElements.via(new FormatAsTextFn()))
// Make key-value pairs with the first letter as the key
.apply(ParDo.of(new FirstLetterAsKey()))
// Group the words by first letter
.apply(GroupByKey.<String, String> create())
// Get a PCollection of only the values, discarding the keys
.apply(ParDo.of(new GetValues()))
// Write the words to the database
.apply(JdbcIO.<String> writeIterable()
.withDataSourceConfiguration(
JdbcIO.DataSourceConfiguration.create(options.getJdbcDriver(), options.getURL()))
.withStatement(INSERT_OR_UPDATE_SQL)
.withPreparedStatementSetter(new WordCountPreparedStatementSetter()));
The difference with the normal write-method of JdbcIO is the new method writeIterable() that takes a PCollection<Iterable<RowT>> as input instead of PCollection<RowT>. Each Iterable is written as one batch to the database.
The version of JdbcIO with this addition can be found here: https://github.com/olavloite/beam/blob/JdbcIOIterableWrite/sdks/java/io/jdbc/src/main/java/org/apache/beam/sdk/io/jdbc/JdbcIO.java
The entire example project containing the example above can be found here: https://github.com/olavloite/spanner-beam-example
(There is also a pull request pending on Apache Beam to include this in the project)
Related
I am trying to export a large feature collection from GEE. I realize that the Python API allows for this more easily than the Java does, but given a time constraint on my research, I'd like to see if I can extract the feature collection in pieces and then append the separate CSV files once exported.
I tried to use a filtering function to perform the task, one that I've seen used before with image collections. Here is a mini example of what I am trying to do
Given a feature collection of 10 spatial points called "points" I tried to create a new feature collection that includes only the first five points:
var points_chunk1 = points.filter(ee.Filter.rangeContains('system:index', 0, 5));
When I execute this function, I receive the following error: "An internal server error has occurred"
I am not sure why this code is not executing as expected. If you know more than I do about this issue, please advise on alternative approaches to splitting my sample, or on where the error in my code lurks.
Many thanks!
system:index is actually ID given by GEE for the feature and it's not supposed to be used like index in an array. I think JS should be enough to export a large featurecollection but there is a way to do what you want to do without relying on system:index as that might not be consistent.
First, it would be a good idea to know the number of features you are dealing with. This is because generally when you use size().getInfo() for large feature collections, the UI can freeze and sometimes the tab becomes unresponsive. Here I have defined chunks and collectionSize. It should be defined in client side as we want to do Export within the loop which is not possible in server size loops. Within the loop, you can simply creating a subset of feature starting from different points by converting the features to list and changing the subset back to feature collection.
var chunk = 1000;
var collectionSize = 10000
for (var i = 0; i<collectionSize;i=i+chunk){
var subset = ee.FeatureCollection(fc.toList(chunk, i));
Export.table.toAsset(subset, "description", "/asset/id")
}
I am working on migration of 3.0 code into new 4.2 framework. I am facing a few difficulties:
How to do CDR level deduplication in new 4.2 framework? (Note: Table deduplication is already done).
Where to implement PostDedupProcessor - context or chainsink custom? In either case, do I need to remove duplicate hashcodes from the list or just reject the tuples? Here I am also doing column updating for a few tuples.
My file is not moving into archive. The temporary output file is getting generated and that too empty and outside load directory. What could be the possible reasons? - I have thoroughly checked config parameters and after putting logs, it seems correct output is being sent from transformer custom, so I don't know where it is stuck. I had printed TableRowGenerator stream for logs(end of DataProcessor).
1. and 2.:
You need to select the type of deduplication. It is not a big difference if you choose "table-" or "cdr-level-deduplication".
The ite.businessLogic.transformation.outputType does affect this. There is one Dedup only. You can not have both.
Select recordStream for "cdr-level-deduplication", do the transformation to table row format (e.g. if you like to use the TableFileWriter) in xxx.chainsink.custom::PostContextDataProcessor.
In xxx.chainsink.custom::PostContextDataProcessor you need to add custom code for duplicate-handling: reject (discard) tuples or set special column values or write them to different target tables.
3.:
Possibly reasons could be:
Missing forwarding of window punctuations or statistic tuple
error in BloomFilter configuration, you would see it easily because PE is down and error log gives hints about wrong sha2 functions be used
To troubleshoot your ITE application, I recommend to enable the following debug sinks if checking the StreamsStudio live graph is not sufficient:
ite.businessLogic.transformation.debug=on
ite.businessLogic.group.debug=on
ite.businessLogic.sink.debug=on
Run a test with a single input file only and check the flow of your record and statistic tuples. "Debug sinks" write punctuations markers also to debug files.
I want to insert documents into Couchbase as a bulk in Java. So what is the standard procedure for Generating keys for each document in java..?
You could use a Couchbase "counter" document as a form of sequence. Using the reactive approach with the Java SDK, this would go something like this, assuming your batch is a List<JsonObject> with each content to save to Couchbase:
//start with a sequence of contents to save
Observable.from(listOfDocumentContent)
//for each content, asynchronously generate something...
.flatMap(content -> bucket.async() //assuming bucket is a `Bucket`
//atomically generate an increasing value, starting from 0
.counter("DOCUMENT_KEY_GENERATOR", 1, 0) //use a more relevant document key
//this gives a `JsonLongDocument`, so extract the number and turn that + the original content into a `JsonDocument` to be saved
.map(cDoc -> JsonDocument.create(KEY_PREFIX + cDoc.content(), content))
)
//next up, asynchronously saving each document we generated in the previous step... You could also use insert since you don't expect the keys to already exist in Couchbase
.flatMap(docToSave -> bucket.async().upsert(docToSave))
//this will perform the above asynchronously but wait for the last doc in the batch to finish saving:
.toBlocking().last();
Notice we use a KEY_PREFIX when generating the document to be saved, so that there is less risk of collision (otherwise, other documents in the bucket could be named "1" if you do that for multiple types of documents inside the same bucket).
Also tune the saving method used to your needs (here upsert vs create vs update, TTL, durability requirements, etc...)
Consider a running Hadoop job, in which a custom InputFormat needs to communicate ("return", similarly to a callback) a few simple values to the driver class (i.e., to the class that has launched the job), from within its overriden getSplits() method, using the new mapreduce API (as opposed to mapred).
These values should ideally be returned in-memory (as opposed to saving them to HDFS or to the DistributedCache).
If these values were only numbers, one could be tempted to use Hadoop counters. However, in numerous tests counters do not seem to be available at the getSplits() phase and anyway they are restricted to numbers.
An alternative could be to use the Configuration object of the job, which, as the source code reveals, should be the same object in memory for both the getSplits() and the driver class.
In such a scenario, if the InputFormat wants to "return" a (say) positive long value to the driver class, the code would look something like:
// In the custom InputFormat.
public List<InputSplit> getSplits(JobContext job) throws IOException
{
...
long value = ... // A value >= 0
job.getConfiguration().setLong("value", value);
...
}
// In the Hadoop driver class.
Job job = ... // Get the job to be launched
...
job.submit(); // Start running the job
...
while (!job.isComplete())
{
...
if (job.getConfiguration().getLong("value", -1))
{
...
}
else
{
continue; // Wait for the value to be set by getSplits()
}
...
}
The above works in tests, but is it a "safe" way of communicating values?
Or is there a better approach for such in-memory "callbacks"?
UPDATE
The "in-memory callback" technique may not work in all Hadoop distributions, so, as mentioned above, a safer way is, instead of saving the values to be passed back in the Configuration object, create a custom object, serialize it (e.g., as JSON), saved it (in HDFS or in the distributed cache) and have it read in the driver class. I have also tested this approach and it works as expected.
Using the configuration is a perfectly suitable solution (admittedly for a problem I'm not sure I understand), but once the job has actually been submitted to the Job tracker, you will not be able to amend this value (client side or task side) and expect to see the change on the opposite side of the comms (setting configuration values in a map task for example will not be persisted to the other mappers, nor to the reducers, nor will be visible to the job tracker).
So to communicate information back from within getSplits back to your client polling loop (to see when the job has actually finished defining the input splits) is fine in your example.
What's your greater aim or use case for using this?
I want to process multiline CSV files and for that I wrote a custom CSVInputFormat.
I would like to have about 40 threads processing CSV lines on each hadoop node. However, when I create a cluster on Amazon EMR with 5 machines (1 master and 4 cores), I can see I get only 2 map tasks running, even if there are 6 available map slots:
I implemented getSplits in my inputFormat so it would behave like NLineInputFormat. I was expecting with this I would get more thing running in parallel, but have had no effect. Also, I tried setting arguments -s,mapred.tasktracker.map.tasks.maximum=10 --args -jobconf,mapred.map.tasks=10, but no effect.
What can I do to have lines being processed in parallel? The way hadoop is running, it's not scalable, as doesn't matter how many instances I allocate to the cluster, only two map tasks will run at most.
UPDATE:
When I use a non compressed file (zip) as origin, it create more map tasks, about 17 for 1.3 million rows. Even so, I wonder why it shouldn't be more and why more mappers aren't created when data is zipped.
Change the split size to have more splits.
Configuration conf= new Cofiguration();
//set the value that increases your number of splits.
conf.set("mapred.max.split.size", "1020");
Job job = new Job(conf, "My job name");