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Run a For-each loop on a Filtered HashMap

I am so new to java. and there is my problem.
I have a Map in Type of Map<Integer , List<MyObject>> that I call it myMap.
As myMap has a lot of members (About 100000) , I don't think the for loop to be such a good idea so I wanna filter my Map<Integer , List<MyObject>> Where the bellow condition happens:
myMap.get(i).get(every_one_of_them).a_special_attribute_of_my_MyObject == null;
in which every_one_of_them means i wanna to delete members of myMap which the Whole list's members(All of its Objects) are null in that attribute(for more comfort , let's call it myAttribute).
one of my uncompleted idea was such a thing:
Map<Integer, List<toHandle>> collect = myMap.entrySet().stream()
.filter(x -> x.getValue.HERE_IS_WHERE_I_DO_NOT_KNOW_HOW_TO)
.collect(Collectors.toMap(x -> x.getKey(), x -> x.getValue()));
Any Help Will Be Highly Appreciated. Thanks.

You can
iterate over map values() and remove from it elements which you don't want. You can use for that removeIf(Predicate condition).
To check if all elements in list fulfill some condition you can use list.stream().allMatch(Predicate condition)
For instance lets we have Map<Integer, List<String>> and we want to remove lists which have all strings starting with b or B. You can do it via
myMap.values()
.removeIf(list -> list.stream()
.allMatch(str -> str.toLowerCase().startsWith("b"))
// but in real application for better performance use
// .allMatch(str -> str.regionMatches(true, 0, "b", 0, 1))
);
DEMO:
Map<Integer , List<String>> myMap = new HashMap<>(Map.of(
1, List.of("Abc", "Ab"),
2, List.of("Bb", "Bc"),
3, List.of("Cc")
));
myMap.values()
.removeIf(list -> list.stream()
.allMatch(str -> str.toLowerCase().startsWith("b"))
);
System.out.println(myMap);
Output:
{1=[Abc, Ab], 3=[Cc]}

As myMap has a lot of members (About 100000) , I don't think the for loop to be such a good idea so I wanna filter
That sounds like you think stream.filter is somehow faster than foreach. It's not; it's either slower or about as fast.
SPOILER: All the way at the end I do some basic performance tests, but I invite anyone to take that test and upgrade it to a full JMH test suite and run it on a variety of hardware. However - it says you're in fact exactly wrong, and foreach is considerably faster than anything involving streams.
Also, it sounds like you feel 100000 is a lot of entries. It mostly isn't. a foreach loop (or rather, an iterator) will be faster. Removing with the iterator will be considerably faster.
parallelism can help you out here, and is simpler with streams, but you can't just slap a parallel() in there and trust that it'll just work out. It depends on the underlying types. For example, your plain jane j.u.HashMap isn't very good at this; Something like a ConcurrentHashMap is far more capable. But if you take the time to copy over all data to a more suitable map type, well, in that timespan you could have done the entire job, and probably faster to boot! (Depends on how large those lists are).
Step 1: Make an oracle
But, first things first, we need an oracle function: One that determines if a given entry ought to be deleted. No matter what solution you go with, this is required:
public boolean keep(List<MyObject> mo) {
for (MyObject obj : mo) if (obj.specialProperty != null) return true;
return false;
}
you could 'streamify' it:
public boolean keep(List<MyObject> mo) {
return mo.stream().anyMatch(o -> o.specialProperty != null);
}
Step 2: Filter the list
Once we have that, the task becomes easier:
var it = map.values().iterator();
while (it.hasNext()) if (!keep(it.next())) it.remove();
is now all you need. We can streamify that if you prefer, but note that you can't use streams to change a map 'in place', and copying over is usually considerably slower, so, this is likely slower and certainly takes more memory:
Map<Integer, List<MyObject>> result =
map.entrySet().stream()
.filter(e -> keep(e.getValue()))
.collect(Collectors.toMap(e -> e.getKey(), e -> e.getValue()));
Note also how the stream option doesn't generally result in significantly shorter code either. Don't make the decision between stream or non-stream based on notions that streams are inherently better, or lead to more readable code. Programming just isn't that simple, I'm afraid.
We can also use some of the more functional methods in map itself:
map.values().removeIf(v -> !keep(v));
That seems like the clear winner, here, although it's a bit bizarre we have to 'bounce' through values(); map itself has no removeIf method, but the collections returned by keySet, values, entrySet etc reflect any changes back to the map, so that works out.
Let's performance test!
Performance testing is tricky and really requires using JMH for good results. By all means, as an exercise, do just that. But, let's just do a real quick scan:
import java.util.*;
import java.util.stream.*;
public class Test {
static class MyObj {
String foo;
}
public static MyObj hit() {
MyObj o = new MyObj();
o.foo = "";
return o;
}
public static MyObj miss() {
return new MyObj();
}
private static final int MAP_ELEMS = 100000;
private static final int LIST_ELEMS = 50;
private static final double HIT_OR_MISS = 0.01;
private static final Random rnd = new Random();
public static void main(String[] args) {
var map = construct();
long now = System.currentTimeMillis();
filter_seq(map);
long delta = System.currentTimeMillis() - now;
System.out.printf("Sequential: %.3f\n", 0.001 * delta);
map = construct();
now = System.currentTimeMillis();
filter_stream(map);
delta = System.currentTimeMillis() - now;
System.out.printf("Stream: %.3f\n", 0.001 * delta);
map = construct();
now = System.currentTimeMillis();
filter_removeIf(map);
delta = System.currentTimeMillis() - now;
System.out.printf("RemoveIf: %.3f\n", 0.001 * delta);
}
private static Map<Integer, List<MyObj>> construct() {
var m = new HashMap<Integer, List<MyObj>>();
for (int i = 0; i < MAP_ELEMS; i++) {
var list = new ArrayList<MyObj>();
for (int j = 0; j < LIST_ELEMS; j++) {
list.add(rnd.nextDouble() < HIT_OR_MISS ? hit() : miss());
}
m.put(i, list);
}
return m;
}
static boolean keep_seq(List<MyObj> list) {
for (MyObj o : list) if (o.foo != null) return true;
return false;
}
static boolean keep_stream(List<MyObj> list) {
return list.stream().anyMatch(o -> o.foo != null);
}
static void filter_seq(Map<Integer, List<MyObj>> map) {
var it = map.values().iterator();
while (it.hasNext()) if (!keep_seq(it.next())) it.remove();
}
static void filter_stream(Map<Integer, List<MyObj>> map) {
Map<Integer, List<MyObj>> result =
map.entrySet().stream()
.filter(e -> keep_stream(e.getValue()))
.collect(Collectors.toMap(e -> e.getKey(), e -> e.getValue()));
}
static void filter_removeIf(Map<Integer, List<MyObj>> map) {
map.values().removeIf(v -> !keep_stream(v));
}
}
This, reliably, on my hardware anyway, shows that the stream route is by far the slowest, and the sequential option wins out with some percent from the removeIf variant. Which just goes to show that your initial line (if I can take that as 'I think foreach is too slow') was entirely off the mark, fortunately.
For fun I replaced the map with a ConcurrentHashMap and made the stream parallel(). This did not change the timing significantly, and I wasn't really expecting it too.
A note about style
In various snippets, I omit braces for loops and if statements. If you add them, the non-stream-based code occupies considerably more lines, and if you include the indent whitespace for the insides of these constructs, considerably more 'surface area' of paste. However, that is a ridiculous thing to clue off of - that is tantamount to saying: "Actually, the commonly followed style guides for java are incredibly obtuse and badly considered. However, I dare not break them. Fortunately, lambdas came along and gave me an excuse to toss the entire principle of those style guides right out the window and now pile it all into a single, braceless line, and oh look, lambdas lead to shorter code!". I would assume any reader, armed with this knowledge, can easily pierce through such baloney argumentation. The reasons for those braces primarily involve easier debug breakpointing and easy ways to add additional actions to a given 'code node', and those needs are exactly as important, if not more so, if using streams. If it's okay to one-liner and go brace-free for lambdas, then surely it is okay to do the same to if and for bodies.

Java 8 list to map with stream

I have a List<Item> collection.
I need to convert it into Map<Integer, Item>
The key of the map must be the index of the item in the collection.
I can not figure it out how to do this with streams.
Something like:
items.stream().collect(Collectors.toMap(...));
Any help?
As this question is identified as possible duplicate I need to add that my concrete problem was - how to get the position of the item in the list and put it as a key value

You can create a Stream of the indices using an IntStream and then convert them to a Map :
Map<Integer,Item> map =
IntStream.range(0,items.size())
.boxed()
.collect(Collectors.toMap (i -> i, i -> items.get(i)));

One more solution just for completeness is to use custom collector:
public static <T> Collector<T, ?, Map<Integer, T>> toMap() {
return Collector.of(HashMap::new, (map, t) -> map.put(map.size(), t),
(m1, m2) -> {
int s = m1.size();
m2.forEach((k, v) -> m1.put(k+s, v));
return m1;
});
}
Usage:
Map<Integer, Item> map = items.stream().collect(toMap());
This solution is parallel-friendly and does not depend on the source (you can use list without random access or Files.lines() or whatever).

Don't feel like you have to do everything in/with the stream. I would just do:
AtomicInteger index = new AtomicInteger();
items.stream().collect(Collectors.toMap(i -> index.getAndIncrement(), i -> i));
As long as you don't parallelise the stream this will work and it avoids potentially expensive and/or problematic (in the case of duplicates) get() and indexOf() operations.
(You cannot use a regular int variable in place of the AtomicInteger because variables used from outside a lambda expression must be effectively final. Note that when uncontested (as in this case), AtomicInteger is very fast and won't pose a performance problem. But if it worries you you can use a non-thread-safe counter.)

This is updated answer and has none of the problems mentioned in comments.
Map<Integer,Item> outputMap = IntStream.range(0,inputList.size()).boxed().collect(Collectors.toMap(Function.identity(), i->inputList.get(i)));

Using a third party library (protonpack for example, but there are others) you can zip the value with its index and voila:
StreamUtils.zipWithIndex(items.stream())
.collect(Collectors.toMap(Indexed::getIndex, Indexed::getValue));
although getIndex returns a long, so you may need to cast it using something similar to:
i -> Integer.valueOf((int) i.getIndex())

Eran's answer is usually the best approach for random-access lists.
If your List isn't random access, or if you have a Stream instead of a List, you can use forEachOrdered:
Stream<Item> stream = ... ;
Map<Integer, Item> map = new HashMap<>();
AtomicInteger index = new AtomicInteger();
stream.forEachOrdered(item -> map.put(index.getAndIncrement(), item));
This is safe, if the stream is parallel, even though the destination map is thread-unsafe and is operated upon as a side effect. The forEachOrdered guarantees that items are processed one-at-a-time, in order. For this reason it's unlikely that any speedup will result from running in parallel. (There might be some speedup if there are expensive operations in the pipeline before the forEachOrdered.)

How to create a List<T> from Map<K,V> and List<K> of keys?

Using Java 8 lambdas, what's the "best" way to effectively create a new List<T> given a List<K> of possible keys and a Map<K,V>? This is the scenario where you are given a List of possible Map keys and are expected to generate a List<T> where T is some type that is constructed based on some aspect of V, the map value types.
I've explored a few and don't feel comfortable claiming one way is better than another (with maybe one exception -- see code). I'll clarify "best" as a combination of code clarity and runtime efficiency. These are what I came up with. I'm sure someone can do better, which is one aspect of this question. I don't like the filter aspect of most as it means needing to create intermediate structures and multiple passes over the names List. Right now, I'm opting for Example 6 -- a plain 'ol loop. (NOTE: Some cryptic thoughts are in the code comments, especially "need to reference externally..." This means external from the lambda.)
public class Java8Mapping {
private final Map<String,Wongo> nameToWongoMap = new HashMap<>();
public Java8Mapping(){
List<String> names = Arrays.asList("abbey","normal","hans","delbrook");
List<String> types = Arrays.asList("crazy","boring","shocking","dead");
for(int i=0; i<names.size(); i++){
nameToWongoMap.put(names.get(i),new Wongo(names.get(i),types.get(i)));
}
}
public static void main(String[] args) {
System.out.println("in main");
Java8Mapping j = new Java8Mapping();
List<String> testNames = Arrays.asList("abbey", "froderick","igor");
System.out.println(j.getBongosExample1(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
System.out.println(j.getBongosExample2(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
System.out.println(j.getBongosExample3(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
System.out.println(j.getBongosExample4(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
System.out.println(j.getBongosExample5(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
System.out.println(j.getBongosExample6(testNames).stream().map(Bongo::toString).collect(Collectors.joining(", ")));
}
private static class Wongo{
String name;
String type;
public Wongo(String s, String t){name=s;type=t;}
#Override public String toString(){return "Wongo{name="+name+", type="+type+"}";}
}
private static class Bongo{
Wongo wongo;
public Bongo(Wongo w){wongo = w;}
#Override public String toString(){ return "Bongo{wongo="+wongo+"}";}
}
// 1: Create a list externally and add items inside 'forEach'.
// Needs to externally reference Map and List
public List<Bongo> getBongosExample1(List<String> names){
final List<Bongo> listOne = new ArrayList<>();
names.forEach(s -> {
Wongo w = nameToWongoMap.get(s);
if(w != null) {
listOne.add(new Bongo(nameToWongoMap.get(s)));
}
});
return listOne;
}
// 2: Use stream().map().collect()
// Needs to externally reference Map
public List<Bongo> getBongosExample2(List<String> names){
return names.stream()
.filter(s -> nameToWongoMap.get(s) != null)
.map(s -> new Bongo(nameToWongoMap.get(s)))
.collect(Collectors.toList());
}
// 3: Create custom Collector
// Needs to externally reference Map
public List<Bongo> getBongosExample3(List<String> names){
Function<List<Wongo>,List<Bongo>> finisher = list -> list.stream().map(Bongo::new).collect(Collectors.toList());
Collector<String,List<Wongo>,List<Bongo>> bongoCollector =
Collector.of(ArrayList::new,getAccumulator(),getCombiner(),finisher, Characteristics.UNORDERED);
return names.stream().collect(bongoCollector);
}
// example 3 helper code
private BiConsumer<List<Wongo>,String> getAccumulator(){
return (list,string) -> {
Wongo w = nameToWongoMap.get(string);
if(w != null){
list.add(w);
}
};
}
// example 3 helper code
private BinaryOperator<List<Wongo>> getCombiner(){
return (l1,l2) -> {
l1.addAll(l2);
return l1;
};
}
// 4: Use internal Bongo creation facility
public List<Bongo> getBongosExample4(List<String> names){
return names.stream().filter(s->nameToWongoMap.get(s) != null).map(s-> new Bongo(nameToWongoMap.get(s))).collect(Collectors.toList());
}
// 5: Stream the Map EntrySet. This avoids referring to anything outside of the stream,
// but bypasses the lookup benefit from Map.
public List<Bongo> getBongosExample5(List<String> names){
return nameToWongoMap.entrySet().stream().filter(e->names.contains(e.getKey())).map(e -> new Bongo(e.getValue())).collect(Collectors.toList());
}
// 6: Plain-ol-java loop
public List<Bongo> getBongosExample6(List<String> names){
List<Bongo> bongos = new ArrayList<>();
for(String s : names){
Wongo w = nameToWongoMap.get(s);
if(w != null){
bongos.add(new Bongo(w));
}
}
return bongos;
}
}

If namesToWongoMap is an instance variable, you can't really avoid a capturing lambda.
You can clean up the stream by splitting up the operations a little more:
return names.stream()
.map(n -> namesToWongoMap.get(n))
.filter(w -> w != null)
.map(w -> new Bongo(w))
.collect(toList());
return names.stream()
.map(namesToWongoMap::get)
.filter(Objects::nonNull)
.map(Bongo::new)
.collect(toList());
That way you don't call get twice.
This is very much like the for loop, except, for example, it could theoretically be parallelized if namesToWongoMap can't be mutated concurrently.
I don't like the filter aspect of most as it means needing to create intermediate structures and multiple passes over the names List.
There are no intermediate structures and there is only one pass over the List. A stream pipeline says "for each element...do this sequence of operations". Each element is visited once and the pipeline is applied.
Here are some relevant quotes from the java.util.stream package description:
A stream is not a data structure that stores elements; instead, it conveys elements from a source such as a data structure, an array, a generator function, or an I/O channel, through a pipeline of computational operations.
Processing streams lazily allows for significant efficiencies; in a pipeline such as the filter-map-sum example above, filtering, mapping, and summing can be fused into a single pass on the data, with minimal intermediate state.

Radiodef's answer pretty much nailed it, I think. The solution given there:
return names.stream()
.map(namesToWongoMap::get)
.filter(Objects::nonNull)
.map(Bongo::new)
.collect(toList());
is probably about the best that can be done in Java 8.
I did want to mention a small wrinkle in this, though. The Map.get call returns null if the name isn't present in the map, and this is subsequently filtered out. There's nothing wrong with this per se, though it does bake null-means-not-present semantics into the pipeline structure.
In some sense we'd want a mapper pipeline operation that has a choice of returning zero or one elements. A way to do this with streams is with flatMap. The flatmapper function can return an arbitrary number of elements into the stream, but in this case we want just zero or one. Here's how to do that:
return names.stream()
.flatMap(name -> {
Wongo w = nameToWongoMap.get(name);
return w == null ? Stream.empty() : Stream.of(w);
})
.map(Bongo::new)
.collect(toList());
I admit this is pretty clunky and so I wouldn't recommend doing this. A slightly better but somewhat obscure approach is this:
return names.stream()
.flatMap(name -> Optional.ofNullable(nameToWongoMap.get(name))
.map(Stream::of).orElseGet(Stream::empty))
.map(Bongo::new)
.collect(toList());
but I'm still not sure I'd recommend this as it stands.
The use of flatMap does point to another approach, though. If you have a more complicated policy of how to deal with the not-present case, you could refactor this into a helper function that returns a Stream containing the result or an empty Stream if there's no result.
Finally, JDK 9 -- still under development as of this writing -- has added Stream.ofNullable which is useful in exactly these situations:
return names.stream()
.flatMap(name -> Stream.ofNullable(nameToWongoMap.get(name)))
.map(Bongo::new)
.collect(toList());
As an aside, JDK 9 has also added Optional.stream which creates a zero-or-one stream from an Optional. This is useful in cases where you want to call an Optional-returning function from within flatMap. See this answer and this answer for more discussion.

One approach I didn't see is retainAll:
public List<Bongo> getBongos(List<String> names) {
Map<String, Wongo> copy = new HashMap<>(nameToWongoMap);
copy.keySet().retainAll(names);
return copy.values().stream().map(Bongo::new).collect(
Collectors.toList());
}
The extra Map is a minimal performance hit, since it's just copying pointers to objects, not the objects themselves.

Java 8 Streams: Map the same object multiple times based on different properties

I was presented with an interesting problem by a colleague of mine and I was unable to find a neat and pretty Java 8 solution. The problem is to stream through a list of POJOs and then collect them in a map based on multiple properties - the mapping causes the POJO to occur multiple times
Imagine the following POJO:
private static class Customer {
public String first;
public String last;
public Customer(String first, String last) {
this.first = first;
this.last = last;
}
public String toString() {
return "Customer(" + first + " " + last + ")";
}
}
Set it up as a List<Customer>:
// The list of customers
List<Customer> customers = Arrays.asList(
new Customer("Johnny", "Puma"),
new Customer("Super", "Mac"));
Alternative 1: Use a Map outside of the "stream" (or rather outside forEach).
// Alt 1: not pretty since the resulting map is "outside" of
// the stream. If parallel streams are used it must be
// ConcurrentHashMap
Map<String, Customer> res1 = new HashMap<>();
customers.stream().forEach(c -> {
res1.put(c.first, c);
res1.put(c.last, c);
});
Alternative 2: Create map entries and stream them, then flatMap them. IMO it is a bit too verbose and not so easy to read.
// Alt 2: A bit verbose and "new AbstractMap.SimpleEntry" feels as
// a "hard" dependency to AbstractMap
Map<String, Customer> res2 =
customers.stream()
.map(p -> {
Map.Entry<String, Customer> firstEntry = new AbstractMap.SimpleEntry<>(p.first, p);
Map.Entry<String, Customer> lastEntry = new AbstractMap.SimpleEntry<>(p.last, p);
return Stream.of(firstEntry, lastEntry);
})
.flatMap(Function.identity())
.collect(Collectors.toMap(
Map.Entry::getKey, Map.Entry::getValue));
Alternative 3: This is another one that I came up with the "prettiest" code so far but it uses the three-arg version of reduce and the third parameter is a bit dodgy as found in this question: Purpose of third argument to 'reduce' function in Java 8 functional programming. Furthermore, reduce does not seem like a good fit for this problem since it is mutating and parallel streams may not work with the approach below.
// Alt 3: using reduce. Not so pretty
Map<String, Customer> res3 = customers.stream().reduce(
new HashMap<>(),
(m, p) -> {
m.put(p.first, p);
m.put(p.last, p);
return m;
}, (m1, m2) -> m2 /* <- NOT USED UNLESS PARALLEL */);
If the above code is printed like this:
System.out.println(res1);
System.out.println(res2);
System.out.println(res3);
The result would be:
{Super=Customer(Super Mac), Johnny=Customer(Johnny Puma), Mac=Customer(Super Mac), Puma=Customer(Johnny Puma)}
{Super=Customer(Super Mac), Johnny=Customer(Johnny Puma), Mac=Customer(Super Mac), Puma=Customer(Johnny Puma)}
{Super=Customer(Super Mac), Johnny=Customer(Johnny Puma), Mac=Customer(Super Mac), Puma=Customer(Johnny Puma)}
So, now to my question: How should I, in a Java 8 orderly fashion, stream through the List<Customer> and then somehow collect it as a Map<String, Customer> where you split the whole thing as two keys (first AND last) i.e. the Customer is mapped twice. I do not want to use any 3rd party libraries, I do not want to use a map outside of the stream as in alt 1. Are there any other nice alternatives?
The full code can be found on hastebin for simple copy-paste to get the whole thing running.

I think your alternatives 2 and 3 can be re-written to be more clear:
Alternative 2:
Map<String, Customer> res2 = customers.stream()
.flatMap(
c -> Stream.of(c.first, c.last)
.map(k -> new AbstractMap.SimpleImmutableEntry<>(k, c))
).collect(toMap(Map.Entry::getKey, Map.Entry::getValue));
Alternative 3: Your code abuses reduce by mutating the HashMap. To do mutable reduction, use collect:
Map<String, Customer> res3 = customers.stream()
.collect(
HashMap::new,
(m,c) -> {m.put(c.first, c); m.put(c.last, c);},
HashMap::putAll
);
Note that these are not identical. Alternative 2 will throw an exception if there are duplicate keys while Alternative 3 will silently overwrite the entries.
If overwriting entries in case of duplicate keys is what you want, I would personally prefer Alternative 3. It is immediately clear to me what it does. It most closely resembles the iterative solution. I would expect it to be more performant as Alternative 2 has to do a bunch of allocations per customer with all that flatmapping.
However, Alternative 2 has a huge advantage over Alternative 3 by separating the production of entries from their aggregation. This gives you a great deal of flexibility. For example, if you want to change Alternative 2 to overwrite entries on duplicate keys instead of throwing an exception, you would simply add (a,b) -> b to toMap(...). If you decide you want to collect matching entries into a list, all you would have to do is replace toMap(...) with groupingBy(...), etc.

Java 8 Map KeySet Stream not working as desired for use in Collector

I have been trying to learn Java 8's new functional interface features, and I am having some difficulty refactoring code that I have previously written.
As part of a test case, I want to store a list of read names in a Map structure in order to check to see if those reads have been "fixed" in a subsequent section of code. I am converting from an existing Map> data structure. The reason why I am flattening this datastructure is because the outer "String" key of the original Map is not needed in the subsequent analysis (I used it to segregate data from different sources before merging them in the intermediate data). Here is my original program logic:
public class MyClass {
private Map<String, Map<String, Short>> anchorLookup;
...
public void CheckMissingAnchors(...){
Map<String, Boolean> anchorfound = new HashMap<>();
// My old logic used the foreach syntax to populate the "anchorfound" map
for(String rg : anchorLookup.keySet()){
for(String clone : anchorLookup.get(rg).keySet()){
anchorfound.put(clone, false);
}
}
...
// Does work to identify the read name in the file. If found, the boolean in the map
// is set to "true." Afterwards, the program prints the "true" and "false" counts in
// the map
}
}
I attempted to refactor the code to use functional interfaces; however, I getting errors from my IDE (Netbeans 8.0 Patch 2 running Java 1.8.0_05):
public class MyClass {
private Map<String, Map<String, Short>> anchorLookup;
...
public void CheckMissingAnchors(...){
Map<String, Boolean> anchorfound = anchorLookup.keySet()
.stream()
.map((s) -> anchorlookup.get(s).keySet()) // at this point I am expecting a
// Stream<Set<String>> which I thought could be "streamed" for the collector method
// ; however, my IDE does not allow me to select the "stream()" method
.sequential() // this still gives me a Stream<Set<String>>
.collect(Collectors.toMap((s) -> s, (s) -> false);
// I receive an error for the preceding method call, as Stream<Set<String>> cannot be
// converted to type String
...
}
}
Is there a better way to create the "anchorfound" map using the Collection methods or is the vanilla Java "foreach" structure the best way to generate this data structure?
I apologize for any obvious errors in my code. My formal training was not in computer science but I would like to learn more about Java's implementation of functional programming concepts.

I believe what you need is a flatMap.
This way you convert each key of the outer map to a stream of the keys of the corresponding inner map, and then flatten them to a single stream of String.
public class MyClass {
private Map<String, Map<String, Short>> anchorLookup;
...
public void CheckMissingAnchors(...){
Map<String, Boolean> anchorfound = anchorLookup.keySet()
.stream()
.flatMap(s -> anchorlookup.get(s).keySet().stream())
.collect(Collectors.toMap((s) -> s, (s) -> false);
...
}
}

Eran's suggestion of flatMap is a good one, +1.
This can be simplified somewhat by using Map.values() instead of Map.keySet(), since the map's keys aren't used for any other purpose than to retrieve the values. Streaming the result of Map.values() gives a Stream<Map<String,Short>>. Here we don't care about the inner map's values, so we can use keySet() to extract the keys, giving a Stream<Set<String>>. Now we just flatMap these sets into Stream<String>. Finally we send the results into the collector as before.
The resulting code looks like this:
public class MyClass {
private Map<String, Map<String, Short>> anchorLookup;
public void checkMissingAnchors() {
Map<String, Boolean> anchorfound = anchorLookup.values().stream()
.map(Map::keySet)
.flatMap(Set::stream)
.collect(Collectors.toMap(s -> s, s -> false));
}
}