#比如设置为50M：1024*1024*50
fetch.message.max.bytes=52428800
OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions
问题描述：OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions运行SparkStreming程序一段时间后，发现产生了异常：19/06/26 03:05:30 ERROR JobScheduler: Error running job streaming j...
PS4偏移和有效载荷1.76 / 4.05 / 4.55 / 5.01 / 5.05
:fire: PS4Offsets〜如果您需要更新旧的有效负载，请使用这些偏移量。 :fire:
#define KERN_XFAST_SYSCALL 0x30EB30
#define KERN_PROCESS_ASLR 0x2862D6
#define KERN_PRISON_0 0xF26010
#define KERN_ROOTVNODE 0x206D250
#define KERN_PTRACE_CHECK_1 0xAC2F1
#define KERN_PTRACE_CHECK_2 0xAC6A2
#define KERNEL_REGMGR_SETINT 0x4CEAB0
//Reading kernel_base...
void* kernel_base = &((uint8_t*)__readm
spark程序报错后重新启动后报警
22/05/05 10:26:54 ERROR executor.Executor: Exception in task 2.1 in stage 3.0 (TID 37)
org.apache.kafka.clients.consumer.OffsetOutOfRangeException: Offsets out of range with no configured reset policy for partitions: {eventshistory-0
自从把spark 从1.3升级到1.6之后，kafka Streaming相关问题频出。最近又遇到了一个。
job中使用Kafka DirectStream 读取topic中数据，然后做处理。其中有个测试job，停止了几天，再次启动时爆出了kafka.common.OffsetOutOfRangeException。下文记录下异常分析与解决过程。
从字面意思上，说是kafka top...
private SparkKafka kafka = null ;
private static final String TOPIC_SOURCE = "TP_LABEL";
public SparkStoredKuduApp(String[] ar
SETRANGE命令
命令：setrange key offset value，从偏移量offset开始，覆写value（对于新的value长度如果小于旧值从offset到结束的长度时，长度小于的部分会保持不变），并返回当前value的长度
特殊的情况如下：
1.offset偏移量从0开始，如果偏移量小于0，则会报错(error) ERR offset is out of range
2.如果offset的下标大于旧值的长度，中间的部分会用零字节（zerobytes，“\x00
Got fetch request with offset out of range
storm spolt无法从kafka中读取数据，storm-ui日志报错信息Got fetch request with offset out of range ，网上说是偏移量的问题，需要修改zookeeper中对应主题下的偏移量。
1，进入zookeeper客户端命令行。
zkCli.sh -serve...
Caused by: org.apache.kafka.clients.consumer.NoOffsetForPartitionException: Undefined offset with no reset policy for partitions: [test-topic-1]
这是因为设置的auto.offset.reset为none，表示如果在kafka broker中找不到当前消费者组的offset时，则抛出异常。
下面是源码中的解释：
* <code>.
解释以下代码： def LSH(self, hash_buckets, x): #x: [N,H*W,C] N = x.shape[0] device = x.device #generate random rotation matrix rotations_shape = (1, x.shape[-1], self.n_hashes, hash_buckets//2) #[1,C,n_hashes,hash_buckets//2] random_rotations = torch.randn(rotations_shape, dtype=x.dtype, device=device).expand(N, -1, -1, -1) #[N, C, n_hashes, hash_buckets//2] #locality sensitive hashing rotated_vecs = torch.einsum('btf,bfhi->bhti', x, random_rotations) #[N, n_hashes, H*W, hash_buckets//2] rotated_vecs = torch.cat([rotated_vecs, -rotated_vecs], dim=-1) #[N, n_hashes, H*W, hash_buckets] #get hash codes hash_codes = torch.argmax(rotated_vecs, dim=-1) #[N,n_hashes,H*W] #add offsets to avoid hash codes overlapping between hash rounds offsets = torch.arange(self.n_hashes, device=device) offsets = torch.reshape(offsets * hash_buckets, (1, -1, 1)) hash_codes = torch.reshape(hash_codes + offsets, (N, -1,)) #[N,n_hashes*H*W] return hash_codes
