@Test
public void testRandomGrouping() throws Exception {
try {
int indexIter = 50 * RANDOM_MULTIPLIER; // make >0 to enable test
int queryIter = 100 * RANDOM_MULTIPLIER;
while (--indexIter >= 0) {
int indexSize = random.nextInt(25 * RANDOM_MULTIPLIER);
List<FldType> types = new ArrayList<FldType>();
types.add(new FldType("id", ONE_ONE, new SVal('A', 'Z', 4, 4)));
types.add(
new FldType("score_s1", ONE_ONE, new SVal('a', 'c', 1, 1))); // field used to score
types.add(new FldType("bar_s1", ONE_ONE, new SVal('a', 'z', 3, 5)));
types.add(new FldType(FOO_STRING_FIELD, ONE_ONE, new SVal('a', 'z', 1, 2)));
types.add(
new FldType(
SMALL_STRING_FIELD, ZERO_ONE, new SVal('a', (char) ('c' + indexSize / 10), 1, 1)));
clearIndex();
Map<Comparable, Doc> model = indexDocs(types, null, indexSize);
// test with specific docs
if (false) {
clearIndex();
model.clear();
Doc d1 = createDoc(types);
d1.getValues(SMALL_STRING_FIELD).set(0, "c");
d1.getValues(SMALL_INT_FIELD).set(0, 5);
d1.order = 0;
updateJ(toJSON(d1), params("commit", "true"));
model.put(d1.id, d1);
d1 = createDoc(types);
d1.getValues(SMALL_STRING_FIELD).set(0, "b");
d1.getValues(SMALL_INT_FIELD).set(0, 5);
d1.order = 1;
updateJ(toJSON(d1), params("commit", "false"));
model.put(d1.id, d1);
d1 = createDoc(types);
d1.getValues(SMALL_STRING_FIELD).set(0, "c");
d1.getValues(SMALL_INT_FIELD).set(0, 5);
d1.order = 2;
updateJ(toJSON(d1), params("commit", "false"));
model.put(d1.id, d1);
d1 = createDoc(types);
d1.getValues(SMALL_STRING_FIELD).set(0, "c");
d1.getValues(SMALL_INT_FIELD).set(0, 5);
d1.order = 3;
updateJ(toJSON(d1), params("commit", "false"));
model.put(d1.id, d1);
d1 = createDoc(types);
d1.getValues(SMALL_STRING_FIELD).set(0, "b");
d1.getValues(SMALL_INT_FIELD).set(0, 2);
d1.order = 4;
updateJ(toJSON(d1), params("commit", "true"));
model.put(d1.id, d1);
}
for (int qiter = 0; qiter < queryIter; qiter++) {
String groupField = types.get(random.nextInt(types.size())).fname;
int rows =
random.nextInt(10) == 0 ? random.nextInt(model.size() + 2) : random.nextInt(11) - 1;
int start =
random.nextInt(5) == 0
? random.nextInt(model.size() + 2)
: random.nextInt(5); // pick a small start normally for better coverage
int group_limit =
random.nextInt(10) == 0 ? random.nextInt(model.size() + 2) : random.nextInt(11) - 1;
int group_offset =
random.nextInt(10) == 0
? random.nextInt(model.size() + 2)
: random.nextInt(2); // pick a small start normally for better coverage
String[] stringSortA = new String[1];
Comparator<Doc> sortComparator = createSort(h.getCore().getSchema(), types, stringSortA);
String sortStr = stringSortA[0];
Comparator<Doc> groupComparator =
random.nextBoolean()
? sortComparator
: createSort(h.getCore().getSchema(), types, stringSortA);
String groupSortStr = stringSortA[0];
// since groupSortStr defaults to sortStr, we need to normalize null to "score desc" if
// sortStr != null.
if (groupSortStr == null && groupSortStr != sortStr) {
groupSortStr = "score desc";
}
// Test specific case
if (false) {
groupField = SMALL_INT_FIELD;
sortComparator =
createComparator(
Arrays.asList(createComparator(SMALL_STRING_FIELD, true, true, false, true)));
sortStr = SMALL_STRING_FIELD + " asc";
groupComparator =
createComparator(
Arrays.asList(createComparator(SMALL_STRING_FIELD, true, true, false, false)));
groupSortStr = SMALL_STRING_FIELD + " asc";
rows = 1;
start = 0;
group_offset = 1;
group_limit = 1;
}
Map<Comparable, Grp> groups = groupBy(model.values(), groupField);
// first sort the docs in each group
for (Grp grp : groups.values()) {
Collections.sort(grp.docs, groupComparator);
}
// now sort the groups
// if sort != group.sort, we need to find the max doc by "sort"
if (groupComparator != sortComparator) {
for (Grp grp : groups.values()) grp.setMaxDoc(sortComparator);
}
List<Grp> sortedGroups = new ArrayList<Grp>(groups.values());
Collections.sort(
sortedGroups,
groupComparator == sortComparator
? createFirstDocComparator(sortComparator)
: createMaxDocComparator(sortComparator));
boolean includeNGroups = random.nextBoolean();
Object modelResponse =
buildGroupedResult(
h.getCore().getSchema(),
sortedGroups,
start,
rows,
group_offset,
group_limit,
includeNGroups);
boolean truncateGroups = random.nextBoolean();
Map<String, Integer> facetCounts = new TreeMap<String, Integer>();
if (truncateGroups) {
for (Grp grp : sortedGroups) {
Doc doc = grp.docs.get(0);
if (doc.getValues(FOO_STRING_FIELD) == null) {
continue;
}
String key = doc.getFirstValue(FOO_STRING_FIELD).toString();
boolean exists = facetCounts.containsKey(key);
int count = exists ? facetCounts.get(key) : 0;
facetCounts.put(key, ++count);
}
} else {
for (Doc doc : model.values()) {
if (doc.getValues(FOO_STRING_FIELD) == null) {
continue;
}
for (Comparable field : doc.getValues(FOO_STRING_FIELD)) {
String key = field.toString();
boolean exists = facetCounts.containsKey(key);
int count = exists ? facetCounts.get(key) : 0;
facetCounts.put(key, ++count);
}
}
}
List<Comparable> expectedFacetResponse = new ArrayList<Comparable>();
for (Map.Entry<String, Integer> stringIntegerEntry : facetCounts.entrySet()) {
expectedFacetResponse.add(stringIntegerEntry.getKey());
expectedFacetResponse.add(stringIntegerEntry.getValue());
}
int randomPercentage = random.nextInt(101);
// TODO: create a random filter too
SolrQueryRequest req =
req(
"group",
"true",
"wt",
"json",
"indent",
"true",
"echoParams",
"all",
"q",
"{!func}score_f",
"group.field",
groupField,
sortStr == null ? "nosort" : "sort",
sortStr == null ? "" : sortStr,
(groupSortStr == null || groupSortStr == sortStr) ? "noGroupsort" : "group.sort",
groupSortStr == null ? "" : groupSortStr,
"rows",
"" + rows,
"start",
"" + start,
"group.offset",
"" + group_offset,
"group.limit",
"" + group_limit,
GroupParams.GROUP_CACHE_PERCENTAGE,
Integer.toString(randomPercentage),
GroupParams.GROUP_TOTAL_COUNT,
includeNGroups ? "true" : "false",
"facet",
"true",
"facet.sort",
"index",
"facet.limit",
"-1",
"facet.field",
FOO_STRING_FIELD,
GroupParams.GROUP_TRUNCATE,
truncateGroups ? "true" : "false",
"facet.mincount",
"1");
String strResponse = h.query(req);
Object realResponse = ObjectBuilder.fromJSON(strResponse);
String err = JSONTestUtil.matchObj("/grouped/" + groupField, realResponse, modelResponse);
if (err != null) {
log.error(
"GROUPING MISMATCH: "
+ err
+ "\n\trequest="
+ req
+ "\n\tresult="
+ strResponse
+ "\n\texpected="
+ JSONUtil.toJSON(modelResponse)
+ "\n\tsorted_model="
+ sortedGroups);
// re-execute the request... good for putting a breakpoint here for debugging
String rsp = h.query(req);
fail(err);
}
// assert post / pre grouping facets
err =
JSONTestUtil.matchObj(
"/facet_counts/facet_fields/" + FOO_STRING_FIELD,
realResponse,
expectedFacetResponse);
if (err != null) {
log.error(
"GROUPING MISMATCH: "
+ err
+ "\n\trequest="
+ req
+ "\n\tresult="
+ strResponse
+ "\n\texpected="
+ JSONUtil.toJSON(expectedFacetResponse));
// re-execute the request... good for putting a breakpoint here for debugging
h.query(req);
fail(err);
}
} // end query iter
} // end index iter
} finally {
// B/c the facet.field is also used of grouping we have the purge the FC to avoid FC insanity
FieldCache.DEFAULT.purgeAllCaches();
}
}
@Override
public String toString() {
return name + ':' + JSONUtil.toJSON(propMap);
}