I want to understand the behavior of aerospike in different consistancy mode.
Consider a aerospike cluster running with 3 nodes and replication factor 3.
AP modes is simple and it says
Aerospike will allow reads and writes in every sub-cluster.
And Maximum no. of node which can go down < 3 (replication factor)
For aerospike strong consistency it says
Note that the only successful writes are those made on replication-factor number of nodes. Every other write is unsuccessful
Does this really means the no writes are allowed if available nodes < replication factor.
And then same document says
All writes are committed to every replica before the system returns success to the client. In case one of the replica writes fails, the master will ensure that the write is completed to the appropriate number of replicas within the cluster (or sub cluster in case the system has been compromised.)
what does appropriate number of replica means ?
So if I lose one node from my 3 node cluster with strong consistency and replication factor 3 , I will not be able to wright data ?
For aerospike strong consistency it says
Note that the only successful writes are those made on
replication-factor number of nodes. Every other write is unsuccessful
Does this really means the no writes are allowed if available nodes <
replication factor.
Yes, if there are fewer than replication-factor nodes then it is impossible to meet the user specified replication-factor.
All writes are committed to every replica before the system returns
success to the client. In case one of the replica writes fails, the
master will ensure that the write is completed to the appropriate
number of replicas within the cluster (or sub cluster in case the
system has been compromised.)
what does appropriate number of replica means ?
It means replication-factor nodes must receive the write. When a node fails, a new node can be promoted to replica status until either the node returns or an operator registers a new roster (cluster membership list).
So if I lose one node from my 3 node cluster with strong consistency
and replication factor 3 , I will not be able to wright data ?
Yes, so having all nodes a replicas wouldn't be a very useful configuration. Replication-factor 3 allows up to 2 nodes to be down, but only if the remaining nodes are able to satisfy the replication-factor. So for replication-factor 3 you would probably want to run with a minimum of 5 nodes.
You are correct, with 3 nodes and RF 3, losing one node means the cluster will not be able to successfully take write transactions since it wouldn't be able to write the required number of copies (3 in this case).
Appropriate number of replicas means a number of replicas that would match the replication factor configured.
Related
If this sound silly to you I apologise in advance, I am new to splunk and did udemy course but can't figure out this.
If I check my indexes.conf file in cluster master I get repFator=0
#
# By default none of the indexes are replicated.
#
repFactor = 0
but if I check https://:8089/services/cluster/config
I see replication factor :
replication_factor 2
So I am confused whether my data is getting replicated,
I have two indexes in a cluster
I believe replication_factor determines how many replicas to have amongst nodes in the cluster, and refFactor determines whether or not to replicate a particular index.
For repFactor, which is an index specific setting
The indexes.conf repFactor attribute
When you add a new index stanza, you must set the repFactor attribute to "auto". This causes the index's data to be replicated to other peers in the cluster.
Note: By default, repFactor is set to 0, which means that the index will not be replicated. For clustered indexes, you must set it to "auto".
The only valid values for repFactor are 0 and "auto".
For replication_factor, which is a cluster setting:
Replication factor and cluster resiliency
The cluster can tolerate a failure of (replication factor - 1) peer nodes. For example, to ensure that your system can tolerate a failure of two peers, you must configure a replication factor of 3, which means that the cluster stores three identical copies of each bucket on separate nodes. With a replication factor of 3, you can be certain that all your data will be available if no more than two peer nodes in the cluster fail. With two nodes down, you still have one complete copy of data available on the remaining peers.
By increasing the replication factor, you can tolerate more peer node failures. With a replication factor of 2, you can tolerate just one node failure; with a replication factor of 3, you can tolerate two concurrent failures; and so on.
The repFactor setting lets you choose which indexes are replicated. By default, none are. The replication_factor setting says how many copies of a replicated bucket to make. Both must be non-zero to replicate data.
The Cluster Manager should confirm that. Select Settings->Indexer Clustering to see which indexes are replicated and their state.
we have many datacenters but datacenter1 is the main.
the master in datacenter1 is being monitored by sentinel so if the master goes down one the replicas will become master and also all data is being synced continuously.
we want to have one Redis replica in each datacenter, replicate all data from datacenter1 but without the ability to become master. (always get data from data center 1 and just replica 1 have the ability to become master but other replicas must not be able)
is there a Redis config for this or any idea?
Redis Multi Datacenter
Redis config [1] has a replica-priority parameter which should serve your purpose.
The replica priority is an integer number published by Redis in the INFO
output. It is used by Redis Sentinel in order to select a replica to promote
into a master if the master is no longer working correctly.
A replica with a low priority number is considered better for promotion, so
for instance if there are three replicas with priority 10, 100, 25 Sentinel
will pick the one with priority 10, that is the lowest.
However a special priority of 0 marks the replica as not able to perform the
role of master, so a replica with priority of 0 will never be selected by
Redis Sentinel for promotion.
By default the priority is 100.
The idea can be setting lower replica-priority value to replicas in datacenter1 and higher value to replicas in other datacenters.
[1] redis.conf file of Redis version 6.2.6: https://github.com/redis/redis/blob/6.2.6/redis.conf
Where can I find information about the how flow of the read/write request in the cluster when fired from the client API?
In Aerospike configuration doc ( http://www.aerospike.com/docs/reference/configuration ), it's mentioned about transaction queues, service threads, transaction threads etc but they are not discussed in the architecture document. I want to understand how it works so that I can configure it accordingly.
From client to cluster node
In your application, a record's key is the 3-tuple (namespace, set, identifier). The key is passed to the client for all key-value methods (such as get and put).
The client then hashes the (set, identifier) portion of the key through RIPEMD-160, resulting in a 20B digest. This digest is the actual unique identifier of the record within the specified namespace of your Aerospike cluster. Each namespace has 4096 partitions, which are distributed across the nodes of the cluster.
The client uses 12 bits of the digest to determine the partition ID of this specific key. Using the partition map, the client looks up the node that owns the master partition corresponding to the partition ID. As the cluster grows, the cost of finding the correct node stays constant (O(1)) as it does not depended on the number of records or the number of nodes.
The client converts the operation and its data into an Aerospike wire protocol message, then uses an existing TCP connection from its pool (or creates a new one) to send the message to the correct node (the one holding this partition ID's master replica).
Service threads and transaction queues
When an operation message comes in as a NIC transmit/receive queue interrupt,
a service thread picks up the message from the NIC. What happens next depends on the namespace this operation is supposed to execute against. If it is an in-memory namespace, the service thread will perform all of the following steps. If it's a namespace whose data is stored on SSD, the service thread will place the operation on a transaction queue. One of the queue's transaction threads will perform the following steps.
Primary index lookup
Every record has a 64B metadata entry in the in-memory primary index. The primary-index is expressed as a collection of sprigs per-partition, with each sprig being implemented as a red-black tree.
The thread (either a transaction thread or the service thread, as mentioned above) finds the partition ID from the record's digest, and skips to the correct sprig of the partition.
Exist, Read, Update, Replace
If the operation is an exists, a read, an update or a replace, the thread acquires a record lock, during which other operations wait to access the specific sprig. This is a very short lived lock. The thread walks the red-black tree to find the entry with this digest. If the operation is an exists, and the metadata entry does exist, the thread will package the appropriate message and respond. For a read, the thread will use the pointer metadata to read the record from the namespace storage.
An update needs to read the record as described above, and then merge in the bin data. A replace is similar to an update, but it skips first reading the current record. If the namespace is in-memory the service thread will write the modified record to memory. If the namespace stores on SSD the merged record is placed in a streaming write buffer, pending a flush to the storage device. The metadata entry in the primary index is adjusted, updating its pointer to the new location of the record. Aerospike performs a copy-on-write for create/update/replace.
Updates and replaces also needs to be communicated to the replica(s) if the replication factor of the namespace is greater than 1. After the record locking process, the operation will also be parked in the RW Hash (Serializer), while the replica write completes. This is where other transactions on the same record will queue up until they hit the transaction pending limit (AKA a hot key). The replica write(s) is handled by a different thread (rw-receive), releasing the transaction or service thread to move on to the next operation. When the replica writes complete the RW Hash lock is released, and the rw-receive thread will package the reply message and send it back to the client.
Create and Delete
If the operation is a new record being written, or a record being deleted, the partition sprig needs to be modified.
Like update/replace, these operations acquire the record-level lock and will go through the RW hash. Because they add or remove a metadata entry from the red-black tree representing the sprig, they must also acquire the index tree reduction lock. This process also happens when the namespace supervisor thread finds expired records and remove them from the primary index. A create operation will add an element to the partition sprig.
If the namespace stores on SSD, the create will load the record into a streaming write buffer, pending a flush to SSD, and ahead of the replica write. It will update the metadata entry in the primary index, adjusting its pointer to the new block.
A delete removes the metadata entry from the partition sprig of the primary index.
Summary
exists/read grab the record-level lock, and hold it for the shortest amount of time. That's also the case for update/replace when replication factor is 1.
update/replace also grab the RW hash lock, when replication factor is higher than 1.
create/delete also grab the index tree reduction lock.
For in-memory namespaces the service thread does all the work up to potentially the point of replica writes.
For data on SSD namespaces the service thread throws the operation onto a transaction queue, after which one of its transaction threads handles things such as loading the record into a streaming write buffer for writes, up until the potential replica write.
The rw-receive thread deals with replica writes and returning the message after the update/replace/create/delete write operation.
Further reading
I've addressed key-value operations, but not batch, scan or query. The difference between batch-reads and single-key reads is easier to understand once you know how single-read works.
Durable deletes do not remove the metadata entry of this record from the primary index. Instead, those are a new write operation of a tombstone. There will be a new 64B entry in the primary index, and a 128B entry in the SSD for the record.
Performance optimizations with CPU pinning. See: auto-pin, service-threads, transaction-queues.
Service threads == transaction queues == number of cores in your CPU or use CPU pinning - auto-pin config parameter if available in your version and possible in your OS env.
transaction threads per queue-> 3 (default is 4, for objsize <1KB, non data-in-memory namespace, 3 is optimal)
Changes with server ver 4.7+, the transaction is now handled by the service thread itself. By default, number of service threads is now set to 5 x no. of cpu cores. Once a service thread picks a transaction from the socket buffer, it carries it through completion unless it ends up in the rwHash (e.g. writes for replicating). The transaction queue is still there (internally) but only relevant for transaction restarts when queued up in the rwHash. (Multiple pending transactions for the same digest).
We've installed Datastax on five nodes with search enabled on the five nodes and replication factor of 3. After adding 590 rows to a table and select from node 1 it retrieve 590. And when selecting from other nodes the number varies from 570 to 585 rows.
I tried using CONSISTENCY QUORUM on cqlsh, but nothing changed. And solr_query is not supported on CONSISTENCY QUORUM.
Is there a way to assure all data written to Cassandra is relieved as it is?
As LHWizard mentioned, if you use Consistency levels such that (nodes_written + nodes_read) > RF, you will ensure immediate consistency.
In your case, you can try using a CONSISTENCY ALL on your read so that all nodes are checked before returning (this will be immediately consistent even with write CL of ONE). This should actually trigger a read repair on the inconsistent nodes and the missing data will be streamed to those nodes.
You're right that solr queries can only be read at CL ONE. If you need higher consistency requirements, you will need to raise the CL for the writes to achieve what you need.
TL;DR
If a replica node goes down and new partition map is not available yet, will a read with consistency level = ALL fail?
Example:
Given this Aerospike cluster setup:
- 3 physical nodes: A, B, C
- Replicas = 2
- Read consistency level = ALL (reads consult both nodes holding the data)
And this sequence of events:
- A piece of data "DAT" is stored into two nodes, A and B
- Node B goes down.
- Immediately after B goes down, a read request ("request 1") is performed with consistency ALL.
- After ~1 second, a new partition map is generated. The cluster is now aware that B is gone.
- "DAT" now becomes replicated at node C (to preserve replicas=2).
- Another read request ("request 2") is performed with consistency ALL.
It is reasonable to say "request 2" will succeed.
Will "request 1" succeed? Will it:
a) Succeed because two reads were attempted, even if one node was down?
b) Fail because one node was down, meaning only 1 copy of "DAT" was available?
Request 1 and request 2 will succeed. The behavior of the consistency level policies are described here: https://discuss.aerospike.com/t/understanding-consistency-level-overrides/711.
The gist for read/write consistency levels is that they only apply when there are multiple versions of a given partition within the cluster. If there is only one version of a given partition in the cluster then a read/write will only go to a single node regardless of the consistency level.
So given an Aerospike cluster of A,B,C where A is master and B is
replica for partition 1.
Assume B fails and C is now replica for partition 1. Partition 1
receives a write and the partition key is changed.
Now B is restarted and returns to the cluster. Partition 1 on B will
now be different from A and C.
A read arrives with consistency all to node A for a key on Partition
1 and there are now 2 versions of that partition in the cluster. We
will read the record from nodes A and B and return the latest
version (not fail the read).
Time lapse
Migrations are now complete, for partition 1, A is master, B is
replica, and C no longer has the partition.
A read arrives with consistency all to node A. Since there is only
one version of Partition 1, node A responds to the client without
consulting node B.