According to official docs of ClickHouse there's the request for monitoring that could inform me if something wrong with my replica.
SELECT
database,
table,
is_leader,
is_readonly,
is_session_expired,
future_parts,
parts_to_check,
columns_version,
queue_size,
inserts_in_queue,
merges_in_queue,
log_max_index,
log_pointer,
total_replicas,
active_replicas
FROM system.replicas
WHERE
is_readonly
OR is_session_expired
OR future_parts > 20
OR parts_to_check > 10
OR queue_size > 20
OR inserts_in_queue > 10
OR log_max_index - log_pointer > 10
OR total_replicas < 2
OR active_replicas < total_replicas
Have I additionaly monitoring leader state of my replica? What happens with replica if leader get down and there's no one node which could be leader instead (can_become_leader: 0)?
What happens with replica if leader get down and there's no one node which could be leader instead (can_become_leader: 0)?
Merges and mutations will stop. You will get the error "too many parts".
Related
I am trying to use clickhouse keeper as per the given article https://kb.altinity.com/altinity-kb-setup-and-maintenance/altinity-kb-zookeeper/clickhouse-keeper/
When I replace the hostname1 and hostname2 with my remote server ips, I'm getting error as->
RaftInstance: total 1 nodes (including this node) responded for pre-vote (term 0, live 0, dead 1), at least 2 nodes should respond
RaftInstance: peer (2) response error: failed to connect to peer 2, x.x.x.x:9444
RaftInstance: Election timeout, initiate leader election
Able to complete configuration with https://clickhouse.com/docs/en/guides/sre/clickhouse-keeper
But whenever running a query on cluster getting error
DB::Exception: Received from localhost:9000.
DB::Exception: Watching task /clickhouse/task_queue/ddl/query-0000000003
is executing longer than distributed_ddl_task_timeout (=180) seconds.
There are 2 unfinished hosts (0 of them are currently active),
they are going to execute the query in background. (TIMEOUT_EXCEEDED)
After running
FROM system.clusters
is_local for nodes is 0
It's an issue with the port configuration. This GitHub issue should answer this question: https://github.com/ClickHouse/ClickHouse/issues/36750
I have a Aerospike (3.11.1.1) cluster with 6 nodes. When I try to add a new node, sometimes some objects are "temporarily" lost while the cluster is migrating data. After the migration finishes, the missing data return. Is this a BUG or am I doing something wrong? How to avoid
Notices that while migration happens, the master object count is lower then the actual final object count after migration finishes
Master and replica count before finishing migrations:
Master and replica count after finishing migrations:
My aerospike.conf:
service {
user root
group root
paxos-single-replica-limit 1 # Number of nodes where the replica count is automatically reduced to 1.
paxos-recovery-policy auto-reset-master
pidfile /var/run/aerospike/asd.pid
service-threads 32
transaction-queues 32
transaction-threads-per-queue 4
batch-index-threads 40
proto-fd-max 15000
batch-max-requests 30000
replication-fire-and-forget true
}
logging {
# Log file must be an absolute path.
file /var/log/aerospike/aerospike.log {
context any info
}
}
network {
service {
#address any
port 3000
}
heartbeat {
mode mesh
mesh-seed-address-port 10.240.0.32 3002
mesh-seed-address-port 10.240.0.33 3002
port 3002
interval 150
timeout 20
}
fabric {
port 3001
}
info {
port 3003
}
}
namespace mynamespace {
replication-factor 2
memory-size 1500M
default-ttl 0 # 30 days, use 0 to never expire/evict.
ldt-enabled true
write-commit-level-override master
storage-engine device {
file /data/aerospike.dat
#device /dev/sdb
write-block-size 1M
filesize 280G
}
}
Some of the discrepancy was due to an issue in the original migration/rebalance design and is addressed in the protocol change in Aerospike 3.13. Prior to the protocol change in 3.13, when running replication-factor 2, the operator must upgrade one node at a time and wait for migrations to complete afterwards.
Additional discrepancy is Aerospike avoiding over counting master-objects and replica objects (i.e. prole-objects) during migration. Also with 3.13 we added a stat for the non-replica-objects which are objects that are not currently acting as master or replica. These are either (a) objects on a partition that has inbound migrations and will eventually act as replica or (b) these are objects on a partition that will not participate and will be dropped when migrations terminate for the partition.
Prior to 3.13, non-replica-object of type (a) would reduce the counts for both master-objects or prole-objects. This is because prior to the protocol change, when a partition returns that was previously master, it immediately resumes as master even though it doesn't have the new writes that took place while it was away. This isn't optimal behavior but it isn't losing data since we will resolve the missing records from the non-replica-objects on other nodes. Post protocol change, a returning 'master' partition will not resume as 'master' until it has received all migrations from other nodes.
Prior to 3.13, non-replica-objects of type (b) would immediately drop and would reduce the count for prole-objects. This causes the replication-factor of records written while a node was away to be reduced by one (e.g. replication-factor 2 temporarily becomes replication-factor 1). This is also the reason it was important to wait for migrations to complete before proceeding to upgrade the next node. Post protocol change (unless running in-memory only), it is no longer necessary to wait for migrations to complete between node upgrades because the interim 'subset partitions' aren't dropped which prevents record's replication-factor from being reduced (actually, with the new protocol, during migrations there are often replication-factor + 1 copies of a record).
I've got the following configuration:
Redis_version:3.2.0
3 master nodes and 3 slave nodes
Each master node is replicated to a slave Everything is correct. When one master node fails by a "kill" command, the corresponding slave node becomes the master as expected. After few seconds, cluster_state returns to the OK state.
BUT, if two master nodes fail simultaneously, none of the associated slave nodes become the master. The cluster_state stays in "fail" state.
cluster nodes command output.
b60c284a515b31aa6b11022fc07cf1a399171e04 127.0.0.1:7000 master,fail? - 1464690455030 1464690454930 1 disconnected 0-5460
637d1f074419963653b206c5ed7cbed4c3d0ace0 127.0.0.1:7001 master,fail? - 1464690455030 1464690454930 2 disconnected 5461-10922
d2aae2a3d87c6407e002076740c8febf80f37865 127.0.0.1:7003 myself,slave b60c284a515b31aa6b11022fc07cf1a399171e04 0 0 4 connected
72d4c9ce140fb57436c1b21702bf3c646ef29db3 127.0.0.1:7002 master - 0 1464690718480 3 connected 10923-16383
af34a7b2241943baf23e634e81b552d8bf23cdd0 127.0.0.1:7005 slave 72d4c9ce140fb57436c1b21702bf3c646ef29db3 0 1464690718480 6 connected
d0fec0609c9e786ac9ca4629f36cabd7c5c3130c 127.0.0.1:7004 slave 637d1f074419963653b206c5ed7cbed4c3d0ace0 0 1464690718480 5 connected
The slave auto-failover won't happen when at least half of the masters get disconnected, because the failover election is required more than half of the masters come into consensus.
To start a manual failover, connect to the slave node with redis-cli and send a cluster failover TAKEOVER command (the takeover is required).
In your case
redis-cli -h 127.0.0.1 -p 7003 cluster failover takeover
After the :7003 becomes a master, the other slave will start an automatic failover as well since there are more than half (2/3) of the masters are alive.
I can't see the difference between two parameters for the recovery phase of the gateway module.
In the documentation :
The gateway.recover_after_nodes setting (which accepts a number) controls after how many (...) eligible nodes (...) recovery will start.
The gateway.expected_nodes allows to set how many (...) eligible nodes are expected to be in the cluster, and once met, (...) recovery starts
From what I understand, these two settings trigger the recovery phase once the number of node is equal to the value set.
Why using one over the other?
And what is the point of using both of them?
For example :
gateway:
recover_after_nodes: 3
expected_nodes: 5
In this case, what is the purpose of expected_nodes? recovery will be triggered as soon as there will be 3 nodes. There must be another reason to use it.
I hope my question is clear enough.
Thanks in advance!
When using recovery_after_nodes, recover_after_data_nodes or recovery_after_master_nodes, once all set conditions are met the cluster will then start waiting recover_after_time before starting recovery:
The gateway.recover_after_time setting (which accepts a time value)
sets the time to wait till recovery happens once all
gateway.recover_after...nodes conditions are met.
When using expected_nodes, expected_data_nodes or expected_master nodes, recovery will start once all conditions are met - the cluster will not wait. In addition, it will also default recovery_after_time to 5 min.
In your test case:
gateway:
recover_after_nodes: 3
expected_nodes: 5
Once you hit 3 nodes a countdown clock starts and the cluster will then recovery in either 5 minutes (the default) or if you hit 5 nodes. Basically it allows you to set a minimum threshold (recovery_after_nodes), with a timeout (recovery_after_time) to wait for a desired state (expected_nodes). You will either recovery recovery_after_time after recovery_after_nodes is hit, or when expected_nodes is hit (no additional waiting) - whichever comes first.
from the public document, there are misunderstandings in this threads.
http://www.elastic.co/guide/en/elasticsearch/reference/1.x/modules-gateway.html
gateway:
recover_after_time: 5m
expected_nodes: 2
In an expected 2 nodes cluster will cause recovery to start 5 minutes
after the first node is up, but once there are 2 nodes in the cluster,
recovery will begin immediately (without waiting).
so, the timer defined by recover_after_time will start already after a first node is up. not start after finding nodes defined in recover_after_nodes
We are trying to run a cassandra cluster on AWS/EC2 within a standard VPC footprint (cassandra nodes on private subnets). Because this is AWS there is always a chance that an EC2 instance will terminate or reboot with no warning. I have been simulating this case on a test cluster and I am seeing things with the cluster that I thought a cluster was suppose to prevent. Specifically if a node reboots some data will go temporarily missing until the node completes its reboot. If a node terminates it appears that some data is lost forever.
For my test I just did a bunch of writes (using QUORUM consistency) to some keyspaces then interrogate the contents of those keyspaces as I bring down nodes (either through reboot or terminate). I'm just using cqlsh SELECT to do the keyspace/column family interrogation of the cluster using ONE consistency level.
Note, even though I am performing no writes to the cluster while I am doing the SELECTs rows temporarily disappear when rebooting and can permanently go missing during termination.
I thought Netflix Priam might be able to help, but sadly it doesn't work in a VPC the last time I checked.
Also, because we are using ephemeral storage instances there is no equivalent of 'shutdown' so I cannot run any scripts during reboot/terminate of an instance to perform a nodetool decommission or nodetool removenode before an instance goes away. Terminate is the equivalent of kicking the plug out of the wall.
Since I am using a replication factor of 3 and quorum/write that should mean that all data is written to at least 2 nodes. So, unless I am totally misunderstanding things (which is possible), losing one node should not mean that I lose any data for any period of time when I am using consistency level ONE for the read.
Questions
Why wouldn't a 6 node cluster with a replication factor of 3 work?
Do I need to run something like a 12 node cluster with a replication factor of 7? Don't bother telling me that will fix the problem, because it doesn't.
Do I need to use consistency level of ALL on the writes then use ONE or QUORUM on the reads?
Is there something not quite right with virtual nodes? unlikely
Are there nodetool commands besides removenode that I need to run when a node terminates to recover missing data? As mentioned earlier, when a reboot occurs, eventually the missing data reappears.
Is there some cassandra savant who can look at my cassandra.yaml file below and send me on the path to salvation?
More Info added 7/19
I don't think this is a QUORUM vs ONE vs ALL is the issue. The test I set up performs no writes to the keyspaces after the initial population of the column families. So the data has had plenty of time (hours) to make it to all the nodes as required by the replication factor. Plus the test dataset is REALLY small (2 column families with about 300-1000 values each). So in other words, the data is completely static.
The behavior I am seeing seems to be tied to the fact that the ec2 instance is no longer on the network. The reason I say this is because if I log on to a node and just do a cassandra stop I see no loss of data. But if I do the reboot or terminate I start getting the following in a stack trace.
CassandraHostRetryService - Downed Host Retry service started with queue size -1 and retry delay 10s
CassandraHostRetryService - Downed Host retry shutdown complete
CassandraHostRetryService - Downed Host retry shutdown hook called
Caused by: TimedOutException()
Caused by: TimedOutException()
So it seems to be more of a networking communication issue in that the cluster is expecting, for example 10.0.12.74, to be on the network after it has joined the cluster. If that ip is suddenly unreachable either due to reboot or termination the timeouts start happening.
When I do a nodetool status under all three scenarios (cassandra stop, reboot or terminate) the status of the node shows up as DN. Which is what you would expect. Eventually nodetool status will return to UN with cassandra start or reboot, but obviously termination always stays DN.
Details of my Configuration
Here are some details of my configuration (cassandra.yaml is at the bottom of this posting):
Nodes are running in private subnets of a VPC.
Cassandra 1.2.5 with num_tokens: 256 (virtual nodes). initial_token: (blank). I am really hoping this works because all of our nodes run in autoscaling groups so the thought that redistribution could be handle dynamically is appealing.
EC2 m1.large one seed and one non-seed node in each availability zone. (so 6 total nodes in the cluster).
Ephemeral storage, not EBS.
Ec2Snitch with NetworkTopologyStrategy and all keyspaces have replication factor of 3.
Non-seed nodes are auto_bootstraped, seed nodes are not.
sample cassandra.yaml file
cluster_name: 'TestCluster'
num_tokens: 256
initial_token:
hinted_handoff_enabled: true
max_hint_window_in_ms: 10800000
hinted_handoff_throttle_in_kb: 1024
max_hints_delivery_threads: 2
authenticator: org.apache.cassandra.auth.AllowAllAuthenticator
authorizer: org.apache.cassandra.auth.AllowAllAuthorizer
partitioner: org.apache.cassandra.dht.Murmur3Partitioner
disk_failure_policy: stop
key_cache_size_in_mb:
key_cache_save_period: 14400
row_cache_size_in_mb: 0
row_cache_save_period: 0
row_cache_provider: SerializingCacheProvider
saved_caches_directory: /opt/company/dbserver/caches
commitlog_sync: periodic
commitlog_sync_period_in_ms: 10000
commitlog_segment_size_in_mb: 32
seed_provider:
- class_name: org.apache.cassandra.locator.SimpleSeedProvider
parameters:
- seeds: "SEED_IP_LIST"
flush_largest_memtables_at: 0.75
reduce_cache_sizes_at: 0.85
reduce_cache_capacity_to: 0.6
concurrent_reads: 32
concurrent_writes: 8
memtable_flush_queue_size: 4
trickle_fsync: false
trickle_fsync_interval_in_kb: 10240
storage_port: 7000
ssl_storage_port: 7001
listen_address: LISTEN_ADDRESS
start_native_transport: false
native_transport_port: 9042
start_rpc: true
rpc_address: 0.0.0.0
rpc_port: 9160
rpc_keepalive: true
rpc_server_type: sync
thrift_framed_transport_size_in_mb: 15
thrift_max_message_length_in_mb: 16
incremental_backups: true
snapshot_before_compaction: false
auto_bootstrap: AUTO_BOOTSTRAP
column_index_size_in_kb: 64
in_memory_compaction_limit_in_mb: 64
multithreaded_compaction: false
compaction_throughput_mb_per_sec: 16
compaction_preheat_key_cache: true
read_request_timeout_in_ms: 10000
range_request_timeout_in_ms: 10000
write_request_timeout_in_ms: 10000
truncate_request_timeout_in_ms: 60000
request_timeout_in_ms: 10000
cross_node_timeout: false
endpoint_snitch: Ec2Snitch
dynamic_snitch_update_interval_in_ms: 100
dynamic_snitch_reset_interval_in_ms: 600000
dynamic_snitch_badness_threshold: 0.1
request_scheduler: org.apache.cassandra.scheduler.NoScheduler
index_interval: 128
server_encryption_options:
internode_encryption: none
keystore: conf/.keystore
keystore_password: cassandra
truststore: conf/.truststore
truststore_password: cassandra
client_encryption_options:
enabled: false
keystore: conf/.keystore
keystore_password: cassandra
internode_compression: all
I think http://www.datastax.com/documentation/cassandra/1.2/cassandra/dml/dml_config_consistency_c.html will clear up a lot of this. In particular, QUORUM/ONE is not guaranteed to return the most recent data. QUORUM/QUORUM is. So is ALL/ONE, but that will be intolerant to failure on write.
Edit to go with the new information:
CassandraHostRetryService is part of Hector. I assumed you were testing with cqlsh like a sane person would. Lessons:
Use cqlsh for testing
Use the DataStax Java Driver for building your application, which is faster, easier to use, and has more insight into the cluster state than Hector thanks to the native protocol it's built on.