I'm working on a process that checks and updates data from Oracle database. I'm using hibernate and spring framework in my application.
The application reads a csv file, processes the content, then persiste entities :
public class Main() {
Input input = ReadCSV(path);
EntityList resultList = Process.process(input);
WriteResult.write(resultList);
...
}
// Process class that loops over input
public class Process{
public EntityList process(Input input) :
EntityList results = ...;
...
for(Line line : input.readLine()){
results.add(ProcessLine.process(line))
...
}
return results;
}
// retrieving and updating entities
Class ProcessLine {
#Autowired
DomaineRepository domaineRepository;
#Autowired
CompanyDomaineService companydomaineService
#Transactional
public MyEntity process(Line line){
// getcompanyByXX is CrudRepository method with #Query that returns an entity object
MyEntity companyToAttach = domaineRepository.getCompanyByCode(line.getCode());
MyEntity companyToDetach = domaineRepository.getCompanyBySiret(line.getSiret());
if(companyToDetach == null || companyToAttach == null){
throw new CustomException("Custom Exception");
}
// AttachCompany retrieves some entity relationEntity, then removes companyToDetach and adds CompanyToAttach. this updates relationEntity.company attribute.
companydomaineService.attachCompany(companyToAttach, companyToDetach);
return companyToAttach;
}
}
public class WriteResult{
#Autowired
DomaineRepository domaineRepository;
#Transactional
public void write(EntityList results) {
for (MyEntity result : results){
domaineRepository.save(result)
}
}
}
The application works well on files with few lines, but when i try to process large files (200 000 lines), the performance slows drastically, and i get a SQL timeout.
I suspect cache issues, but i'm wondering if saving all the entities at the end of the processing isn't a bad practice ?
The problem is your for loop which is doing individual saves on the result and thus does single inserts slowing it down. Hibernate and spring support batch inserts and should be done when ever possible.
something like domaineRepository.saveAll(results)
Since you are processing lot of data it might be better to do things in batches so instead of getting one company to attach you should get a list of companies to attach processes those then get a list of companies to detach and process those
public EntityList process(Input input) :
EntityList results;
List<Code> companiesToAdd = new ArrayList<>();
List<Siret> companiesToRemove = new ArrayList<>();
for(Line line : input.readLine()){
companiesToAdd.add(line.getCode());
companiesToRemove.add(line.getSiret());
...
}
results = process(companiesToAdd, companiesToRemove);
return results;
}
public MyEntity process(List<Code> companiesToAdd, List<Siret> companiesToRemove) {
List<MyEntity> attachList = domaineRepository.getCompanyByCodeIn(companiesToAdd);
List<MyEntity> detachList = domaineRepository.getCompanyBySiretIn(companiesToRemove);
if (attachList.isEmpty() || detachList.isEmpty()) {
throw new CustomException("Custom Exception");
}
companydomaineService.attachCompany(attachList, detachList);
return attachList;
}
The above code is just sudo code to point you in the right direction, will need to work out what works for you.
For every line you read you are doing 2 read operations here
MyEntity companyToAttach = domaineRepository.getCompanyByCode(line.getCode());
MyEntity companyToDetach = domaineRepository.getCompanyBySiret(line.getSiret());
You can read more than one line and us the in query and then process that list of companies
Related
I have an application where I am managing documents. I would like to ask you whether I need to deal with concurrency.
Lets say, I will have the method below (which is in the class with #Service and #Transactional) and more requests would come which would require to use this function.
Will spring and database handle concurrency without synchronization? (my db is MySQL and JPA). So the first request to use this method will be executed, but another request will wait till the previous request will be done... so it would not happen that something would be overwritten in the database...
Thanks for help
public void updateSharing(long userId, long documentId, int approval) {
Optional<Document> optionalDocument = documentRepository.findById(documentId);
User user = userService.findUserById(userId);
if(optionalDocument.isPresent()){
Document document = optionalDocument.get();
if(document.getDocumentState().getId() == 2){
documentRepository.updateSharing(userId, documentId, approval);
if(approval == 0){
List<User> users = userService.getUsersForApprovingDocument(documentId);
Map<String, String> map = emailService.createMessage(2, user, document);
if(document.getUser().isActive()){
users.add(document.getUser());
}
setDocumentType(documentId, 3);
sendEmail(users, map.get("subject"), map.get("message"));
} else if(isDocumentApproved(documentId)){
setDocumentType(documentId, 1);
List<User> users = userService.getUsersForApprovingDocument(documentId);
if(document.getUser().isActive()){
users.add(document.getUser());
}
Map<String, String> map = emailService.createMessage(5, user, document);
sendEmail(users, map.get("subject"), map.get("message"));
}
} else if(document.getDocumentState().getId() == 1){
documentRepository.updateSharing(userId, documentId, approval);
} else {
return;
}
}
}
You don't need to deal with concurrency in this situation.
For every request, the container creates a new Thread and each Thread has it's own stack.
I am trying to develop a batch process using Spring Batch + Spring Boot (Java config), but I have a problem doing so. I have a software that has a database and a Java API, and I read records from there. The batch process should retrieve all the documents which expiration date is less than a certain date, update the date, and save them again in the same database.
My first approach was reading the records 100 by 100; so the ItemReader retrieve 100 records, I process them 1 by 1, and finally I write them again. In the reader, I put this code:
public class DocumentItemReader implements ItemReader<Document> {
public List<Document> documents = new ArrayList<>();
#Override
public Document read() throws Exception, UnexpectedInputException, ParseException, NonTransientResourceException {
if(documents.isEmpty()) {
getDocuments(); // This method retrieve 100 documents and store them in "documents" list.
if(documents.isEmpty()) return null;
}
Document doc = documents.get(0);
documents.remove(0);
return doc;
}
}
So, with this code, the reader reads from the database until no records are found. When the "getDocuments()" method doesn't retrieve any documents, the List is empty and the reader returns null (so the Job finish). Everything worked fine here.
However, the problem appears if I want to use several threads. In this case, I started using the Partitioner approach instead of Multi-threading. The reason of doing that is because I read from the same database, so if I repeat the full step with several threads, all of them will find the same records, and I cannot use pagination (see below).
Another problem is that database records are updated dynamically, so I cannot use pagination. For example, let's suppose I have 200 records, and all of them are going to expire soon, so the process is going to retrieve them. Now imagine I retrieve 10 with one thread, and before anything else, that thread process one and update it in the same database. The next thread cannot retrieve from 11 to 20 records, as the first record is not going to appear in the search (as it has been processed, its date has been updated, and then it doesn't match the query).
It is a little difficult to understand, and some things may sound strange, but in my project:
I am forced to use the same database to read and write.
I can have millions of documents, so I cannot read all the records at the same time. I need to read them 100 by 100, or 500 by 500.
I need to use several threads.
I cannot use pagination, as the query to the databse will retrieve different documents each time it is executed.
So, after hours thinking, I think the unique possible solution is to repeat the job until the query retrives no documents. Is this possible? I want to do something like the step does: Do something until null is returned - repeat the job until the query return zero records.
If this is not a good approach, I will appreciate other possible solutions.
Thank you.
Maybe you can add a partitioner to your step that will :
Select all the ids of the datas that needs to be updated (and other columns if needed)
Split them in x (x = gridSize parameter) partitions and write them in temporary file (1 by partition).
Register the filename to read in the executionContext
Then your reader is not reading from the database anymore but from the partitioned file.
Seem complicated but it's not that much, here is an example which handle millions of record using JDBC query but it can be easily transposed for your use case :
public class JdbcToFilePartitioner implements Partitioner {
/** number of records by database fetch */
private int fetchSize = 100;
/** working directory */
private File tmpDir;
/** limit the number of item to select */
private Long nbItemMax;
#Override
public Map<String, ExecutionContext> partition(final int gridSize) {
// Create contexts for each parttion
Map<String, ExecutionContext> executionsContexte = createExecutionsContext(gridSize);
// Fill partition with ids to handle
getIdsAndFillPartitionFiles(executionsContexte);
return executionsContexte;
}
/**
* #param gridSize number of partitions
* #return map of execution context, one for each partition
*/
private Map<String, ExecutionContext> createExecutionsContext(final int gridSize) {
final Map<String, ExecutionContext> map = new HashMap<>();
for (int partitionId = 0; partitionId < gridSize; partitionId++) {
map.put(String.valueOf(partitionId), createContext(partitionId));
}
return map;
}
/**
* #param partitionId id of the partition to create context
* #return created executionContext
*/
private ExecutionContext createContext(final int partitionId) {
final ExecutionContext context = new ExecutionContext();
String fileName = tmpDir + File.separator + "partition_" + partitionId + ".txt";
context.put(PartitionerConstantes.ID_GRID.getCode(), partitionId);
context.put(PartitionerConstantes.FILE_NAME.getCode(), fileName);
if (contextParameters != null) {
for (Entry<String, Object> entry : contextParameters.entrySet()) {
context.put(entry.getKey(), entry.getValue());
}
}
return context;
}
private void getIdsAndFillPartitionFiles(final Map<String, ExecutionContext> executionsContexte) {
List<BufferedWriter> fileWriters = new ArrayList<>();
try {
// BufferedWriter for each partition
for (int i = 0; i < executionsContexte.size(); i++) {
BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(executionsContexte.get(String.valueOf(i)).getString(
PartitionerConstantes.FILE_NAME.getCode())));
fileWriters.add(bufferedWriter);
}
// Fetching the datas
ScrollableResults results = runQuery();
// Get the result and fill the files
int currentPartition = 0;
int nbWriting = 0;
while (results.next()) {
fileWriters.get(currentPartition).write(results.get(0).toString());
fileWriters.get(currentPartition).newLine();
currentPartition++;
nbWriting++;
// If we already write on all partitions, we start again
if (currentPartition >= executionsContexte.size()) {
currentPartition = 0;
}
// If we reach the max item to read we stop
if (nbItemMax != null && nbItemMax != 0 && nbWriting >= nbItemMax) {
break;
}
}
// closing
results.close();
session.close();
for (BufferedWriter bufferedWriter : fileWriters) {
bufferedWriter.close();
}
} catch (IOException | SQLException e) {
throw new UnexpectedJobExecutionException("Error writing partition file", e);
}
}
private ScrollableResults runQuery() {
...
}
}
I already went through many links like: Spring Batch - Skip Record On Process and simply looking to validate the records in the processor before writing it to the MongoDB.
I've 500 records in the Oracle DB and on 162th record, below code's line-1 satisfy and after than no other records are getting considered for writing, so instead of 500 records, I supposed to get 480 records, 20 records I want to skip because its EFFECTIVE_DATE is null which I don't want to consider for writting.
public class StudentRowMapper implements RowMapper<Student> {
#Override
public Student mapRow(ResultSet rs, int rowNum) throws SQLException {
if(rs.getString("EFFECTIVE_DATE") == null) { //Line-1
return null;
}
else {
Student Student = new Student();
Student.setRowIdObject(rs.getInt("PK_ID"));
.............
.............
.............
.............
return Student;
}
}
}
Aggreed with #Mahmoud, you can also :
Add this filter on the query of your mongodb reader : "{ EFFECTIVE_DATE: null }"
Return null in your processor
simply looking to validate the records in the processor before writing it to the MongoDB.
ValidatingItemProcessor is what you are looking for. It allows you to validate items and skip them or filter them (see filter parameter) before passing them to the writer.
During code optimization I found few areas where I was using findOne() within for loop –
public List<User> validateUsers(List<String> userIds) {
List<User> validUsers = new ArrayList<>();
for ( String userId : userIds) {
User user = userRepository.findOne(userId); //Network hit :: expensive call
//Perform validations
...
//Add valid users to validUsers list
...
}
return validUsers;
}
Above method takes long time if I pass huge list of users to validate. [for 300 users around 5 sec.]
Then I changed above method to use findAll() and perform validations on result collection -
public List<User> validateUsers(List<String> userIds) {
List<User> validUsers = new ArrayList<>();
Iterable<User> itr = userRepository.findAll(userIds); //Only one Network hit
for ( User user : itr) {
//Perform validations
...
//Add valid users to validUsers list
...
}
return validUsers;
}
Now for 300 users, results coming in 100 ms.
Question is: Is there any side effects of using findAll() considering the underlying structure of Cassandra? Also I am using CrudRepository. Should I use CassandraRepository?
Following are the parameters to think of when you are attempting this.
How big is the users table, if you are using findAll.
Partition keys for the user table
As Cassandra queries are faster with the primary key fields, findOne might perform better with the large amount of data.
However, can you try
List<T> findAllById(Iterable<ID> ids);
from org.springframework.data.cassandra.repository.CassandraRepository
I am retrieving big chunks of data from DB and using this data to write it somewhere else. In order to avoid a long processing time, I'm trying to use parallel streams to write it. When I run this as sequential streams, it works perfectly. However, if I change it to parallel, the behavior is odd: it prints the same object multiple times (more than 10).
#PostConstruct
public void retrieveAllTypeRecords() throws SQLException {
logger.info("Retrieve batch of Type records.");
try {
Stream<TypeRecord> typeQueryAsStream = jdbcStream.getTypeQueryAsStream();
typeQueryAsStream.forEach((type) -> {
logger.info("Printing Type with field1: {} and field2: {}.", type.getField1(), type.getField2()); //the same object gets printed here multiple times
//write this object somewhere else
});
logger.info("Completed full retrieval of Type data.");
} catch (Exception e) {
logger.error("error: " + e);
}
}
public Stream<TypeRecord> getTypeQueryAsStream() throws SQLException {
String sql = typeRepository.getQueryAllTypesRecords(); //retrieves SQL query in String format
TypeMapper typeMapper = new TypeMapper();
JdbcStream.StreamableQuery query = jdbcStream.streamableQuery(sql);
Stream<TypeRecord> stream = query.stream()
.map(row -> {
return typeMapper.mapRow(row); //maps columns values to object values
});
return stream;
}
public class StreamableQuery implements Closeable {
(...)
public Stream<SqlRow> stream() throws SQLException {
final SqlRowSet rowSet = new ResultSetWrappingSqlRowSet(preparedStatement.executeQuery());
final SqlRow sqlRow = new SqlRowAdapter(rowSet);
Supplier<Spliterator<SqlRow>> supplier = () -> Spliterators.spliteratorUnknownSize(new Iterator<SqlRow>() {
#Override
public boolean hasNext() {
return !rowSet.isLast();
}
#Override
public SqlRow next() {
if (!rowSet.next()) {
throw new NoSuchElementException();
}
return sqlRow;
}
}, Spliterator.CONCURRENT);
return StreamSupport.stream(supplier, Spliterator.CONCURRENT, true); //this boolean sets the stream as parallel
}
}
I've also tried using typeQueryAsStream.parallel().forEach((type) but the result is the same.
Example of output:
[ForkJoinPool.commonPool-worker-1] INFO TypeService - Saving Type with field1: L6797 and field2: P1433.
[ForkJoinPool.commonPool-worker-1] INFO TypeService - Saving Type with field1: L6797 and field2: P1433.
[main] INFO TypeService - Saving Type with field1: L6797 and field2: P1433.
[ForkJoinPool.commonPool-worker-1] INFO TypeService - Saving Type with field1: L6797 and field2: P1433.
Well, look at you code,
final SqlRow sqlRow = new SqlRowAdapter(rowSet);
Supplier<Spliterator<SqlRow>> supplier = () -> Spliterators.spliteratorUnknownSize(new Iterator<SqlRow>() {
…
#Override
public SqlRow next() {
if (!rowSet.next()) {
throw new NoSuchElementException();
}
return sqlRow;
}
}, Spliterator.CONCURRENT);
You are returning the same object every time. You achieve your desired effects by implicitly modifying the state of this object when calling rowSet.next().
This obviously can’t work when multiple threads try to access that single object concurrently. Even buffering some items, to hand them over to another thread will cause trouble. Therefore, such interference can cause problems with sequential streams as well, as soon as stateful intermediate operations are involved, like sorted or distinct.
Assuming that typeMapper.mapRow(row) will produce an actual data item which has no interference to other data items, you should integrate this step into the stream source, to create a valid stream.
public Stream<TypeRecord> stream(TypeMapper typeMapper) throws SQLException {
SqlRowSet rowSet = new ResultSetWrappingSqlRowSet(preparedStatement.executeQuery());
SqlRow sqlRow = new SqlRowAdapter(rowSet);
Spliterator<TypeRecord> sp = new Spliterators.AbstractSpliterator<TypeRecord>(
Long.MAX_VALUE, Spliterator.CONCURRENT|Spliterator.ORDERED) {
#Override
public boolean tryAdvance(Consumer<? super TypeRecord> action) {
if(!rowSet.next()) return false;
action.accept(typeMapper.mapRow(sqlRow));
return true;
}
};
return StreamSupport.stream(sp, true); //this boolean sets the stream as parallel
}
Note that for a lot of use cases, like this one, implementing a Spliterator is simpler than implementing an Iterator (which needs to be wrapped via spliteratorUnknownSize anyway). Also, there is no need to encapsulate this instantiation into a Supplier.
As a final note, the current implementation does not perform well for streams with an unknown size, as it treats Long.MAX_VALUE like a very large number, ignoring the “unknown” semantic assigned to it by the specification. It will be very beneficial to the parallel performance to provide an estimate size, it doesn’t need to be precise, in fact, with the current implementation, even a completely made up number, say 1000 may perform better than correctly using Long.MAX_VALUE to denote an entirely unknown size.