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Java 8 Streams peek api
(4 answers)
How to use Streams api peek() function and make it work?
(2 answers)
Closed 4 years ago.
I've got 2 statements, I expected that they should "print" same result:
Arrays.stream("abc".split("")).forEach(System.out::println);//first
Arrays.stream("abc".split("")).peek(new Consumer<String>() {//second
#Override
public void accept(String s) {
System.out.println(s);//breakpoint
}
});
In fact, the first statement will print
a
b
c
Ok, but the second statement prints nothing. I tried to set a breakpoint in the line of "//breakpoint" inside IntelliJ, but it wasn't hit.
So how should I change the second statement to use "peek" as it create a new stream while processing every element using "Consumer"?
Thanks a lot.
Stream.peek, as stated in the javadocs of the API as well, is meant mainly for debugging purposes and performing any update operations on the stream during the peek operation is not recommended.
For example, you can verify the intermediate stream state with the following code and what it eventually results in:
Arrays.stream("acb".split(""))
.peek(System.out::println) // print a c b
.sorted()
.forEach(System.out::println); // print a b c
In general, this operation is an intermediate operation wouldn't be executed unless and terminal operation is performed on the stream as mentioned in the Stream operations and pipelines section of the docs and that is exactly the reason why your first statement will print.
Note: Though as suggested in a few other answers, the action within peek
is not invoked in the cases when its able to optimize the result for some short-circuiting operations like findFirst etc.
In cases where the stream implementation is able to optimize away the
production of some or all the elements (such as with short-circuiting
operations like findFirst, or in the example described in count()),
the action will not be invoked for those elements.
peek() is not terminal operation, you need to add any terminal operation to make peek work, e.g.
Arrays.stream("abc".split("")).peek(new Consumer<String>() { //second
#Override
public void accept(String s) {
System.out.println(s);//breakpoint
}
}).count();
The peek() is not a terminal operation, it produces an intermediate stream. Your stream would be executed only when it finds a terminal operation.
For eg: if you add the count() terminal operation to your second stream, you will get the expected output.
Note - You got an output for the first stream because forEach() is a terminal operation.
Stream operations are divided into intermediate (Stream-producing) operations and terminal (value- or side-effect-producing) operations. Intermediate operations are always lazy. So, Steam will start executing the operation pipeline once it gets any terminal operation. In your first case forEach is the terminal operation, so the stream executed. But in the second ca,se the last operation in the pipeline is peek() which is not a terminal operation.
I've gone through several previous questions like Encounter order preservation in java stream, this answer by Brian Goetz, as well as the javadoc for Stream.reduce(), and the java.util.stream package javadoc, and yet I still can't grasp the following:
Take this piece of code:
public static void main(String... args) {
final String[] alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ".split("");
System.out.println("Alphabet: ".concat(Arrays.toString(alphabet)));
System.out.println(new HashSet<>(Arrays.asList(alphabet))
.parallelStream()
.unordered()
.peek(System.out::println)
.reduce("", (a,b) -> a + b, (a,b) -> a + b));
}
Why is the reduction always* preserving the encounter order?
So far, after several dozen runs, output is the same
First of all unordered does not imply an actual shuffling; all it does it sets a flag for the Stream pipeline - that could later be leveraged.
A shuffle of the source elements could potentially be much more expensive then the operations on the stream pipeline themselves, so the implementation might choose not to do this(like in this case).
At the moment (tested and looked at the sources) of jdk-8 and jdk-9 - reduce does not take that into account. Notice that this could very well change in a future build or release.
Also when you say unordered - you actually mean that you don't care about that order and the stream returning the same result is not a violation of that rule.
For example notice this question/answer that explains that findFirst for example (just another terminal operation) changed to take unordered into consideration in java-9 as opposed to java-8.
To help explain this, I am going to reduce the scope of this string to ABCD.
The parallel stream will divide the string into two pieces: AB and CD. When we go to combine these later, the result of the AB side will be the first argument passed into the function, while the result of the CD side will be the second argument passed into the function. This is regardless of which of the two actually finishes first.
The unordered operator will affect some operations on a stream, such as a limit operation, it does not affect a simple reduce.
TLDR: .reduce() is not always preserving order, its result is based on the stream spliterator characteristics.
Spliterator
The encounter order of the stream depends on stream spliterator (None of the answers mentioned that before).
There are different spliterators based on the source stream. You can get the types of spliterators from the source code of those collections.
HashSet -> HashMap#KeySpliterator = Not ordered
ArrayDeque = Ordered
ArrayList = Ordered
TreeSet -> TreeMap#Spliterator = Ordered and sorted
logicbig.com - Ordering
logicbig.com - Stateful vs Stateless
Additionally you can apply .unordered() intermediate stream operation that specifies following operations in the stream should not rely on ordering.
Stream operations (mostly stateful) that are affected by spliterator and usage of .unordered() method are:
.findFirst()
.limit()
.skip()
.distinct()
Those operations will give us different results based on the order property of the stream and its spliterator.
.peek() method does not take ordering into consideration, if stream is executed in parallel it will always print/receive elements in unordered manner.
.reduce()
Now for the terminal .reduce() method. Intermediate operation .unordered() doesn't have any affect on type of spliterator (as #Eugene mentioned). But important notice, it still stays the same as it is in the source spliterator. If source spliterator is ordered, result of the .reduce() will be ordered, if source was unordered result of .reduce() will be unordered.
You are using new HashSet<>(Arrays.asList(alphabet)) to get the instance of the stream. Its spliterator is unordered. It was just a coincidence that you are getting your result ordered because you are using the single alphabet Strings as elements of the stream and unordered result is actually the same. Now if you would mix that with numbers or mix it with lower case and upper case then this doesn't hold true anymore. For example take following inputs, the first one is subset of the example you posted:
HashSet .reduce() - Unordered
"A","B","C","D","E","F" -> "ABCDEF"
"a","b","c","1","2","3","A","B","C" -> "a1Ab2Bc3C"
"Apple","Orange","Banana","Mango" -> "AppleMangoOrangeBanana"
TreeSet .reduce() - Ordered, Sorted
"A","B","C","D","E","F" -> "ABCDEF"
"a","b","c","1","2","3","A","B","C" -> "123ABCabc"
"Apple","Orange","Banana","Mango" -> "AppleBananaMangoOrange"
ArrayList .reduce() - Ordered
"A","B","C","D","E","F" -> "ABCDEF"
"a","b","c","1","2","3","A","B","C" -> "abc123ABC"
"Apple","Orange","Banana","Mango" -> "AppleOrangeBananaMango"
You see that testing .reduce() operation only with an alphabet source stream can lead to false conclusions.
The answer is .reduce() is not always preserving order, its result is based on the stream spliterator characteristics.
stream.parallel().skip(1)
vs
stream.skip(1).parallel()
This is about Java 8 streams.
Are both of these skipping the 1st line/entry?
The example is something like this:
import java.io.BufferedReader;
import java.io.IOException;
import java.io.StringReader;
import java.util.concurrent.atomic.AtomicLong;
public class Test010 {
public static void main(String[] args) {
String message =
"a,b,c\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n1,2,3\n4,5,6\n7,8,9\n";
try(BufferedReader br = new BufferedReader(new StringReader(message))){
AtomicLong cnt = new AtomicLong(1);
br.lines().parallel().skip(1).forEach(
s -> {
System.out.println(cnt.getAndIncrement() + "->" + s);
}
);
}catch (IOException e) {
e.printStackTrace();
}
}
}
Earlier today, I was sometimes getting the header line "a,b,c" in the lambda expression. This was a surprise since I was expecting to have skipped it already. Now I cannot get that example to work i.e. I cannot get the header line in the lambda expression. So I am pretty confused now, maybe something else was influencing that behavior. Of course this is just an example. In the real world the message is being read from a CSV file. The message is the full content of that CSV file.
You actually have two questions in one, the first being whether it makes a difference in writing stream.parallel().skip(1) or stream.skip(1).parallel(), the second being whether either or both will always skip the first element. See also “loaded question”.
The first answer is that it makes no difference, because specifying a .sequential() or .parallel() execution policy affects the entire Stream pipeline, regardless of where you place it in the call chain—of course, unless you specify multiple contradicting policies, in which case the last one wins.
So in either case you are requesting a parallel execution which might affect the outcome of the skip operation, which is subject of the second question.
The answer is not that simple. If the Stream has no defined encounter order in the first place, an arbitrary element might get skipped, which is a consequence of the fact that there is no “first” element, even if there might be an element you encounter first when iterating over the source.
If you have an ordered Stream, skip(1) should skip the first element, but this has been laid down only recently. As discussed in “Stream.skip behavior with unordered terminal operation”, chaining an unordered terminal operation had an effect on the skip operation in earlier implementations and there was some uncertainty of whether this could even be intentional, as visible in “Is this a bug in Files.lines(), or am I misunderstanding something about parallel streams?”, which happens to be close to your code; apparently skipping the first line is a common case.
The final word is that the behavior of earlier JREs is a bug and skip(1) on an ordered stream should skip the first element, even when the stream pipeline is executed in parallel and the terminal operation is unordered. The associated bug report names jdk1.8.0_60 as first fixed version, which I could verify. So if you are using on older implementation, you might experience the Stream skipping different elements when using .parallel() and the unordered .forEach(…) terminal operation. It’s not contradicting if the implementation occasionally skips the expected element, that’s the unpredictability of multi-threading.
So the answer still is that stream.parallel().skip(1) and stream.skip(1).parallel() have the same behavior, even when being used in earlier versions, as both are equally unpredictable when being used with an unordered terminal operation like forEach. They should always skip the first element with ordered Streams and when being used with 1.8.0_60 or newer, they do.
Yes, but skip(n) is slower as n is larger with a parallel stream.
Here's the API note from skip():
While skip() is generally a cheap operation on sequential stream pipelines, it can be quite expensive on ordered parallel pipelines, especially for large values of n, since skip(n) is constrained to skip not just any n elements, but the first n elements in the encounter order. Using an unordered stream source (such as generate(Supplier)) or removing the ordering constraint with BaseStream.unordered() may result in significant speedups of skip() in parallel pipelines, if the semantics of your situation permit. If consistency with encounter order is required, and you are experiencing poor performance or memory utilization with skip() in parallel pipelines, switching to sequential execution with BaseStream.sequential() may improve performance.
So essentially, if you want better performance with skip(), don't use a parellel stream, or use an unordered stream.
As for it seeming to not work with parallel streams, perhaps you're actually seeing that the elements are no longer ordered? For example, an output of this code:
Stream.of("Hello", "How", "Are", "You?")
.parallel()
.skip(1)
.forEach(System.out::println);
Is
Are
You?
How
Ideone Demo
This is perfectly fine because forEach doesn't enforce the encounter order in a parallel stream. If you want it to enforce the encounter order, use a sequential stream (and perhaps use forEachOrdered so that your intent is obvious).
Stream.of("Hello", "How", "Are", "You?")
.skip(1)
.forEachOrdered(System.out::println);
How
Are
You?
I'm wondering if I can add an operation to a stream, based off of some sort of condition set outside of the stream. For example, I want to add a limit operation to the stream if my limit variable is not equal to -1.
My code currently looks like this, but I have yet to see other examples of streams being used this way, where a Stream object is reassigned to the result of an intermediate operation applied on itself:
// Do some stream stuff
stream = stream.filter(e -> e.getTimestamp() < max);
// Limit the stream
if (limit != -1) {
stream = stream.limit(limit);
}
// Collect stream to list
stream.collect(Collectors.toList());
As stated in this stackoverflow post, the filter isn't actually applied until a terminal operation is called. Since I'm reassigning the value of stream before a terminal operation is called, is the above code still a proper way to use Java 8 streams?
There is no semantic difference between a chained series of invocations and a series of invocations storing the intermediate return values. Thus, the following code fragments are equivalent:
a = object.foo();
b = a.bar();
c = b.baz();
and
c = object.foo().bar().baz();
In either case, each method is invoked on the result of the previous invocation. But in the latter case, the intermediate results are not stored but lost on the next invocation. In the case of the stream API, the intermediate results must not be used after you have called the next method on it, thus chaining is the natural way of using stream as it intrinsically ensures that you don’t invoke more than one method on a returned reference.
Still, it is not wrong to store the reference to a stream as long as you obey the contract of not using a returned reference more than once. By using it they way as in your question, i.e. overwriting the variable with the result of the next invocation, you also ensure that you don’t invoke more than one method on a returned reference, thus, it’s a correct usage. Of course, this only works with intermediate results of the same type, so when you are using map or flatMap, getting a stream of a different reference type, you can’t overwrite the local variable. Then you have to be careful to not use the old local variable again, but, as said, as long as you are not using it after the next invocation, there is nothing wrong with the intermediate storage.
Sometimes, you have to store it, e.g.
try(Stream<String> stream = Files.lines(Paths.get("myFile.txt"))) {
stream.filter(s -> !s.isEmpty()).forEach(System.out::println);
}
Note that the code is equivalent to the following alternatives:
try(Stream<String> stream = Files.lines(Paths.get("myFile.txt")).filter(s->!s.isEmpty())) {
stream.forEach(System.out::println);
}
and
try(Stream<String> srcStream = Files.lines(Paths.get("myFile.txt"))) {
Stream<String> tmp = srcStream.filter(s -> !s.isEmpty());
// must not be use variable srcStream here:
tmp.forEach(System.out::println);
}
They are equivalent because forEach is always invoked on the result of filter which is always invoked on the result of Files.lines and it doesn’t matter on which result the final close() operation is invoked as closing affects the entire stream pipeline.
To put it in one sentence, the way you use it, is correct.
I even prefer to do it that way, as not chaining a limit operation when you don’t want to apply a limit is the cleanest way of expression your intent. It’s also worth noting that the suggested alternatives may work in a lot of cases, but they are not semantically equivalent:
.limit(condition? aLimit: Long.MAX_VALUE)
assumes that the maximum number of elements, you can ever encounter, is Long.MAX_VALUE but streams can have more elements than that, they even might be infinite.
.limit(condition? aLimit: list.size())
when the stream source is list, is breaking the lazy evaluation of a stream. In principle, a mutable stream source might legally get arbitrarily changed up to the point when the terminal action is commenced. The result will reflect all modifications made up to this point. When you add an intermediate operation incorporating list.size(), i.e. the actual size of the list at this point, subsequent modifications applied to the collection between this point and the terminal operation may turn this value to have a different meaning than the intended “actually no limit” semantic.
Compare with “Non Interference” section of the API documentation:
For well-behaved stream sources, the source can be modified before the terminal operation commences and those modifications will be reflected in the covered elements. For example, consider the following code:
List<String> l = new ArrayList(Arrays.asList("one", "two"));
Stream<String> sl = l.stream();
l.add("three");
String s = sl.collect(joining(" "));
First a list is created consisting of two strings: "one"; and "two". Then a stream is created from that list. Next the list is modified by adding a third string: "three". Finally the elements of the stream are collected and joined together. Since the list was modified before the terminal collect operation commenced the result will be a string of "one two three".
Of course, this is a rare corner case as normally, a programmer will formulate an entire stream pipeline without modifying the source collection in between. Still, the different semantic remains and it might turn into a very hard to find bug when you once enter such a corner case.
Further, since they are not equivalent, the stream API will never recognize these values as “actually no limit”. Even specifying Long.MAX_VALUE implies that the stream implementation has to track the number of processed elements to ensure that the limit has been obeyed. Thus, not adding a limit operation can have a significant performance advantage over adding a limit with a number that the programmer expects to never be exceeded.
There is two ways you can do this
// Do some stream stuff
List<E> results = list.stream()
.filter(e -> e.getTimestamp() < max);
.limit(limit > 0 ? limit : list.size())
.collect(Collectors.toList());
OR
// Do some stream stuff
stream = stream.filter(e -> e.getTimestamp() < max);
// Limit the stream
if (limit != -1) {
stream = stream.limit(limit);
}
// Collect stream to list
List<E> results = stream.collect(Collectors.toList());
As this is functional programming you should always work on the result of each function. You should specifically avoid modifying anything in this style of programming and treat everything as if it was immutable if possible.
Since I'm reassigning the value of stream before a terminal operation is called, is the above code still a proper way to use Java 8 streams?
It should work, however it reads as a mix of imperative and functional coding. I suggest writing it as a fixed stream as per my first answer.
I think your first line needs to be:
stream = stream.filter(e -> e.getTimestamp() < max);
so that your using the stream returned by filter in subsequent operations rather than the original stream.
I known it is a bit too late, but I had the same question myself and didn't find the satisfying answer, however, inspired by this question and answers I came to the following solution:
return Stream.of( ///< wrap target stream in other stream ;)
/*do regular stream stuff*/
stream.filter(e -> e.getTimestamp() < max)
).flatMap(s -> limit != -1 ? s.limit(limit) : s) ///< apply limit only if necessary and unwrap stream of stream to "normal" stream
.collect(Collectors.toList()) ///< do final stuff
I'm processing a potentially infinite stream of data elements that follow the pattern:
E1 <start mark>
E2 foo
E3 bah
...
En-1 bar
En <end mark>
That is, a stream of <String>s, which must be accumulated in a buffer before I can map them to object model.
Goal: aggregate a Stream<String> into a Stream<ObjectDefinedByStrings> without the overhead of collecting on an infinite stream.
In english, the code would be something like "Once you see a start marker, start buffering. Buffer until you see an end marker, then get ready to return the old buffer, and prepare a fresh buffer. Return the old buffer."
My current implementation has the form:
Data<String>.stream()
.map(functionReturningAnOptionalPresentOnlyIfObjectIsComplete)
.filter(Optional::isPresent)
I have several questions:
What is this operation properly called? (i.e. what can I Google for more examples? Every discussion I find of .map() talks about 1:1 mapping. Every discussion of .reduce) talks about n:1 reduction. Every discussion of .collect() talks about accumulating as a terminal operation...)
This seems bad in many different ways. Is there a better way of implementing this? (A candidate of the form .collectUntilConditionThenApplyFinisher(Collector,Condition,Finisher)...?)
Thanks!
To avoid your kludge you could filter before mapping.
Data<String>.stream()
.filter(text -> canBeConvertedToObject(text))
.map(text -> convertToObject(text))
That works perfectly well on an infinite stream and only constructs objects that need to be constructed. It also avoids the overhead of creating unnecessary Optional objects.
Unfortunately there's no partial reduce operation in Java 8 Stream API. However such operation is implemented in my StreamEx library which enhances standard Java 8 Streams. So your task can be solved like this:
Stream<ObjectDefinedByStrings> result =
StreamEx.of(strings)
.groupRuns((a, b) -> !b.contains("<start mark>"))
.map(stringList -> constructObjectDefinedByStrings());
The strings is normal Java-8 stream or other source like array, Collection, Spliterator, etc. Works fine with infinite or parallel streams. The groupRuns method takes a BiPredicate which is applied to two adjacent stream elements and returns true if these elements must be grouped. Here we say that elements should be grouped unless the second one contains "<start mark>" (which is the start of the new element). After that you will get the stream of List<String> elements.
If collecting to the intermediate lists is not appropriate for you, you can use the collapse(BiPredicate, Collector) method and specify the custom Collector to perform the partial reduction. For example, you may want to join all the strings together:
Stream<ObjectDefinedByStrings> result =
StreamEx.of(strings)
.collapse((a, b) -> !b.contains("<start mark>"), Collectors.joining())
.map(joinedString -> constructObjectDefinedByStrings());
I propose 2 more use cases for this partial reduction:
1. Parsing SQL and PL/SQL (Oracle procedural) statements
Standard delimiter for SQL statements is semicolon (;). It separates normal SQL statements from each other. But if you have PL/SQL statement then semicolon separates operators inside statement from each other, not only statements as whole.
One of the ways of parsing script file containing both normal SQL and PL/SQL statements is to first split them by semicolon and then if particular statement starts with specific keywords (DECLARE, BEGIN, etc.) join this statement with next statements following rules of PL/SQL grammar.
By the way, this cannot be done by using StreamEx partial reduce operations since they only test two adjacent elements. Since you need to know about previous stream elements starting from initial PL/SQL keyword element to determine whether or not to include current element into partial reduction or partial reduction should be finished. In this case mutable partial reduction may be usable with collector holding information of already collected elements and some Predicate testing either only collector itself (if partial reduction should be finished) or BiPredicate testing both collector and current stream element.
In theory, we're speaking about implementing LR(0) or LR(1) parser (see https://en.wikipedia.org/wiki/LR_parser) using Stream pipeline ideology. LR-parser can be used to parse syntax of most programming languages.
Parser is a finite automata with stack. In case of LR(0) automata its transition depends on stack only. In case of LR(1) automata it depends both on stack and next element from the stream (theoretically there can be LR(2), LR(3), etc. automatas peeking 2, 3, etc. next elements to determine transition but in practice all programming languages are syntactically LR(1) languages).
To implement parser there should be a Collector containing stack of finite automata and predicate testing whether final state of this automata is reached (so we can stop reduction). In case of LR(0) it should be Predicate testing Collector itself. And in case of LR(1) it should be BiPredicate testing both Collector and next element from stream (since transition depends on both stack and next symbol).
So to implement LR(0) parser we would need something like following (T is stream elements type, A is accumulator holding both finite automata stack and result, R is result of each parser work forming output stream):
<R,A> Stream<R> Stream<T>.parse(
Collector<T,A,R> automataCollector,
Predicate<A> isFinalState)
(i removed complexity like ? super T instead of T for compactness - result API should contain these)
To implement LR(1) parser we would need something like following:
<R,A> Stream<R> Stream<T>.parse(
BiPredicate<A, T> isFinalState
Collector<T,A,R> automataCollector)
NOTE: In this case BiPredicate should test element before it would be consumed by accumulator. Remember LR(1) parser is peeking next element to determine transition. So there can be a potential exception if empty accumulator rejects to accept next element (BiPredicate returns true, signalizing that partial reduction is over, on empty accumulator just created by Supplier and next stream element).
2. Conditional batching based on stream element type
When we're executing SQL statemens we want to merge adjacent data-modification (DML) statements into a single batch (see JDBC API) to improve overall performance. But we don't want to batch queries. So we need conditional batching (instead of unconditional batching like in Java 8 Stream with batch processing).
For this specific case StreamEx partial reduce operations can be used since if both adjacent elements tested by BiPredicate are DML statements they should be included into batch. So we don't need to know previous history of batch collection.
But we can increase complexity of the task and say that batches should be limited by size. Say, no more than 100 DML statements in a batch. In this case we cannot ignore previous batch collection history and using of BiPredicate to determine whether batch collection should be continued or stopped is insufficient.
Though we can add flatMap after StreamEx partial reduction to split long batches into parts. But this would delay specific 100-element batch execution until all DML statements would be collected into unlimited batch. Needless to say that this is against pipeline ideology: we want to minimize buffering to maximize speed between input and output. Moreover, unlimited batch collection may result in OutOfMemoryError in case of very long list of DML statements without any queries in between (say, million of INSERTs as a result of database export) which is intolerable.
So in case of this complex conditional batch collection with upper limit we also need something as powerful as LR(0) parser described in previous use case.