本文整理汇总了Java中org.apache.hadoop.hbase.io.hfile.TestHFileWriterV2.randomRowOrQualifier方法的典型用法代码示例。如果您正苦于以下问题：Java TestHFileWriterV2.randomRowOrQualifier方法的具体用法？Java TestHFileWriterV2.randomRowOrQualifier怎么用？Java TestHFileWriterV2.randomRowOrQualifier使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.hadoop.hbase.io.hfile.TestHFileWriterV2的用法示例。
在下文中一共展示了TestHFileWriterV2.randomRowOrQualifier方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: readStoreFile
import org.apache.hadoop.hbase.io.hfile.TestHFileWriterV2; //导入方法依赖的package包/类
private void readStoreFile(int t, BloomType bt, List<KeyValue> kvs,
Path sfPath) throws IOException {
StoreFile sf = new StoreFile(fs, sfPath, conf, cacheConf, bt);
StoreFile.Reader r = sf.createReader();
final boolean pread = true; // does not really matter
StoreFileScanner scanner = r.getStoreFileScanner(true, pread);
{
// Test for false negatives (not allowed).
int numChecked = 0;
for (KeyValue kv : kvs) {
byte[] row = kv.getRow();
boolean present = isInBloom(scanner, row, kv.getQualifier());
assertTrue(testIdMsg + " Bloom filter false negative on row "
+ Bytes.toStringBinary(row) + " after " + numChecked
+ " successful checks", present);
++numChecked;
}
}
// Test for false positives (some percentage allowed). We test in two modes:
// "fake lookup" which ignores the key distribution, and production mode.
for (boolean fakeLookupEnabled : new boolean[] { true, false }) {
ByteBloomFilter.setFakeLookupMode(fakeLookupEnabled);
try {
String fakeLookupModeStr = ", fake lookup is " + (fakeLookupEnabled ?
"enabled" : "disabled");
CompoundBloomFilter cbf = (CompoundBloomFilter) r.getGeneralBloomFilter();
cbf.enableTestingStats();
int numFalsePos = 0;
Random rand = new Random(EVALUATION_SEED);
int nTrials = NUM_KV[t] * 10;
for (int i = 0; i < nTrials; ++i) {
byte[] query = TestHFileWriterV2.randomRowOrQualifier(rand);
if (isInBloom(scanner, query, bt, rand)) {
numFalsePos += 1;
}
}
double falsePosRate = numFalsePos * 1.0 / nTrials;
LOG.debug(String.format(testIdMsg
+ " False positives: %d out of %d (%f)",
numFalsePos, nTrials, falsePosRate) + fakeLookupModeStr);
// Check for obvious Bloom filter crashes.
assertTrue("False positive is too high: " + falsePosRate + " (greater "
+ "than " + TOO_HIGH_ERROR_RATE + ")" + fakeLookupModeStr,
falsePosRate < TOO_HIGH_ERROR_RATE);
// Now a more precise check to see if the false positive rate is not
// too high. The reason we use a relaxed restriction for the real-world
// case as opposed to the "fake lookup" case is that our hash functions
// are not completely independent.
double maxZValue = fakeLookupEnabled ? 1.96 : 2.5;
validateFalsePosRate(falsePosRate, nTrials, maxZValue, cbf,
fakeLookupModeStr);
// For checking the lower bound we need to eliminate the last chunk,
// because it is frequently smaller and the false positive rate in it
// is too low. This does not help if there is only one under-sized
// chunk, though.
int nChunks = cbf.getNumChunks();
if (nChunks > 1) {
numFalsePos -= cbf.getNumPositivesForTesting(nChunks - 1);
nTrials -= cbf.getNumQueriesForTesting(nChunks - 1);
falsePosRate = numFalsePos * 1.0 / nTrials;
LOG.info(testIdMsg + " False positive rate without last chunk is " +
falsePosRate + fakeLookupModeStr);
}
validateFalsePosRate(falsePosRate, nTrials, -2.58, cbf,
fakeLookupModeStr);
} finally {
ByteBloomFilter.setFakeLookupMode(false);
}
}
r.close(true); // end of test so evictOnClose
}
示例2: readStoreFile
import org.apache.hadoop.hbase.io.hfile.TestHFileWriterV2; //导入方法依赖的package包/类
private void readStoreFile(int t, BloomType bt, List<KeyValue> kvs,
Path sfPath) throws IOException {
StoreFile sf = new StoreFile(fs, sfPath, conf, cacheConf, bt,
NoOpDataBlockEncoder.INSTANCE);
StoreFile.Reader r = sf.createReader();
final boolean pread = true; // does not really matter
StoreFileScanner scanner = r.getStoreFileScanner(true, pread);
{
// Test for false negatives (not allowed).
int numChecked = 0;
for (KeyValue kv : kvs) {
byte[] row = kv.getRow();
boolean present = isInBloom(scanner, row, kv.getQualifier());
assertTrue(testIdMsg + " Bloom filter false negative on row "
+ Bytes.toStringBinary(row) + " after " + numChecked
+ " successful checks", present);
++numChecked;
}
}
// Test for false positives (some percentage allowed). We test in two modes:
// "fake lookup" which ignores the key distribution, and production mode.
for (boolean fakeLookupEnabled : new boolean[] { true, false }) {
ByteBloomFilter.setFakeLookupMode(fakeLookupEnabled);
try {
String fakeLookupModeStr = ", fake lookup is " + (fakeLookupEnabled ?
"enabled" : "disabled");
CompoundBloomFilter cbf = (CompoundBloomFilter) r.getGeneralBloomFilter();
cbf.enableTestingStats();
int numFalsePos = 0;
Random rand = new Random(EVALUATION_SEED);
int nTrials = NUM_KV[t] * 10;
for (int i = 0; i < nTrials; ++i) {
byte[] query = TestHFileWriterV2.randomRowOrQualifier(rand);
if (isInBloom(scanner, query, bt, rand)) {
numFalsePos += 1;
}
}
double falsePosRate = numFalsePos * 1.0 / nTrials;
LOG.debug(String.format(testIdMsg
+ " False positives: %d out of %d (%f)",
numFalsePos, nTrials, falsePosRate) + fakeLookupModeStr);
// Check for obvious Bloom filter crashes.
assertTrue("False positive is too high: " + falsePosRate + " (greater "
+ "than " + TOO_HIGH_ERROR_RATE + ")" + fakeLookupModeStr,
falsePosRate < TOO_HIGH_ERROR_RATE);
// Now a more precise check to see if the false positive rate is not
// too high. The reason we use a relaxed restriction for the real-world
// case as opposed to the "fake lookup" case is that our hash functions
// are not completely independent.
double maxZValue = fakeLookupEnabled ? 1.96 : 2.5;
validateFalsePosRate(falsePosRate, nTrials, maxZValue, cbf,
fakeLookupModeStr);
// For checking the lower bound we need to eliminate the last chunk,
// because it is frequently smaller and the false positive rate in it
// is too low. This does not help if there is only one under-sized
// chunk, though.
int nChunks = cbf.getNumChunks();
if (nChunks > 1) {
numFalsePos -= cbf.getNumPositivesForTesting(nChunks - 1);
nTrials -= cbf.getNumQueriesForTesting(nChunks - 1);
falsePosRate = numFalsePos * 1.0 / nTrials;
LOG.info(testIdMsg + " False positive rate without last chunk is " +
falsePosRate + fakeLookupModeStr);
}
validateFalsePosRate(falsePosRate, nTrials, -2.58, cbf,
fakeLookupModeStr);
} finally {
ByteBloomFilter.setFakeLookupMode(false);
}
}
r.close(true); // end of test so evictOnClose
}