Page History

The document describes principles used for achieving consistency between data copies for transactional caches with enabled persistence. The information is relevant for AI 2.8 release or higher.

Cluster topology

Cluster topology is a an ordered set of client and server nodes at a certain point in time. Each stable topology topolog is assigned a topology version number - monotonically increasing pair of counters (majorVer, minorVer). Major version is used for tracking grid node events such join, left or fail, minor version is used for tracking internal events such cache start or activation. Each node maintains a list of nodes in the topology, and it is the same on all nodes for a given version. It is important to note that each node receives information about the topology change eventulally, that is, independently of the other node at the different timetopology changes at different times, meaning that at any point in time, nodes can see different topology versions, but eventually they will all see the same.

Affinity function

A set of all possible cache keys is divided between into partitions. A partition is an indivisible data set in a grid, which contains a subset of a the total set of keys. The Each key uniquely determines the partition in which it will be stored.

Every change in topology causes the topology - adding or removing nodes -  creates a new topology version. For each new version of the topology,  it is necessary to calculate how the distribution of data is distributed among between the nodes in topology. Those nodes Nodes that are responsible for storing data are called affinity nodes. Each node owns a subset of partitions. If a new affinity node has joined then it is necessary to rebalance part of the data to joins the cluster,  the data is rebalanced to accommodate the new node. 

The Affinity function defines the distribution (sharding) of data on the nodes of the grid is defined by affinity function. 

First, affinity function uniquely defines a partition for a key:  PP(key) = partition partition.

This is a static property and cannot change at any point in time.

The default implementation of function P is modulo division: abs(key.hashCode) % partitionsCount.

Second, affinity function defines which partitions partition belongs to the which node: F(topology, part) = list(nodes)

At different points in time, a partition can belong to different nodes. This Therefore, this is a dynamic property of a partition.

The number of nodes in this list is equal to the number of copies of the partition. Recall that one of the fundamental properties of a grid is its high availability. With the loss of a certain number of nodes, complete data loss does not occur and the load is not interrupted. The need to store multiple copies is due to the implementation of this property.

The first node in the list is the so-called primary node, which has a special coordinating role for all operations on the partition. The remaining are backup nodes. In the event of a loss of a primary node, one of the backup nodes becomes the new primary.

Todo PIC for some AF calculated state.

An Another important property of affinity function is the uniform distribution of partitions over nodes.

Look at the possible partition distribution between nodes for a topology with three server nodes s1, s2, s3 containing six partitions 0, 1, 2, 3, 4, 5 (partition numeration begins with a zero in Ignite).

Function F could produce an assignment of nodes to each partition, which may look something like this (in the ideal world):

F(0) = s1, s2, s3

F(1) = s2, s3, s1

F(2) = s3, s1, s2

F(3) = s1, s2, s3

F(4) = s2, s3, s1

F(5) = s3, s1, s2

The first node in the list corresponds to the primary node, and other nodes are backups. Only list prefix equal in size to owners count is used, other nodes are ignored. Only those nodes that belong to the prefix list with the length of backups + 1 are used for data placement, others are ignored for partitioned cache.

In a real scenario, the results might not be so ideal because of hash randomization, causing some nodes to have slightly more partitions than others. It depends on total partitions count - the more the total partitions count, the more uniform is the assignment.

The default implementation for function F uses the rendezvous hashing algorithm algorithm https://en.wikipedia.org/wiki/Rendezvous_hashing

Due to the implementation of these properties of affinity function, linear scalability is achieved when adding new nodes to the grid.

Rebalancing

Rebalancing - a is the  procedure for exchanging data between nodes to ensure uniform distribution of data (redistribution of data between nodes) according to affinity assignment [1] and to ensure data consistency if copies diverge [2].

[1] Partition state Affinity based rebalancing

In the first case, the reason for the data rebalancing is a change in the list of nodes - owners of the partition. If a new node has joined joins the topology, then part of the partitions will be reassigned to it due to the property of affinity function property of the that ensures uniform distribution of partitions among between the nodes. If the node is left then the re-balance leaves the topology, then rebalancing will be triggered to ensure the availability of the required configured number of copies of the a partition in the grid. Such kind of rebalancing can only happen when the baseline is changed.

For example, let's consider adding a new node - s4 to the topology. The assignment will look like:

F(0) = s1, s2, s3, s4

F(1) = s2, s3, s4, s1

F(2) = s3, s4, s1, s2

F(3) = s4, s1, s2, s3

F(4) = s1, s2, s3, s4

F(5) = s2, s3, s4, s1

Node s4 will own all 6 partitions and will load them during rebalancing.

[2] Counter Updates counter based rebalancing.

In the second case , which is relevant only for the persistence mode, re-balance when baseline is not changed, rebalancing is used to restore the consistency of the data if the node was absent for some time in the topology under the load.
. This can happen in case of a node crash.When the node re-joins the topology, the , it will have stale data, and a partition update counter will be analyzed to determine how much the partition has have “lagged” from in comparison to other copies.

Depending on how much the partition is partitions are lagging behind, the second type of rebalance rebalancing can use the write-ahead log as a data source for updates history. In this case, the rebalancing can be performed without a complete reloading of the partition and . Such type of rebalancing is called “historical”.

Another scenario for the occurrence of [2] is the activation of the grid , where some nodes may have with nodes  containing stale data.Re-balance

Technically rebalancing is the exchange of supply and demand messages between nodes. A demand message is sent from the node where partition has stale data to the node with that has the actual data. Supply messages is send A supply message containing data is sent in response to the demand message, after . After processing a the  supply message, the next demand message is send sent, and so on, until there is nothing more to supplyrebalance. We will look at this later in more detail.

Update counter structure and flow

Currently, different implementations of update counter logic exist for persistent mode and for in-memory mode. In this article we will only look at former. The main difference with latter is ability to track updates that are  applied out of order.

Each update to a partition receives a sequential number for a purpose of ensuring data consistency between copies. A special structure called partition update counter is used to assign and increment update counters.

Partition A partition update counter has the following structure:

/** Low watermark. */

...

private final AtomicLong cntr

...

 = new AtomicLong();

...

private TreeSet<Item> oomQueue = new TreeSet<>();Low watermark , or update counter is used to track sequential updates. It is only incremented when the corresponding update is recorded to a durable storage, and no missed updates are exist with lesser counter value.

For example, LWM value=5 means what all updates with assigned counters 1, 2, 3, 4, 5 were applied to WAL.

/** High watermark. */
private final AtomicLong reserveCntr = new AtomicLong();

High watermark or reservation counter is increment incremented for each pending update, which even not guaranteed to succeed. Reservation counter is assigned during tx prepare phase.

Given all said, there is an important invariant: HWM >= LWM

/** Updates applied out of order. */
private SortedSet<Range> seq = new TreeSet<>();

This field is used for recording the updates that are Out of order update queue is used, literally, for recording updates applied out of order. This is possible due to a fact what updates this higher reservation because updates with higher counter could be applied to WAL before updates with lesser reservation lower counter, causing gaps in the update sequence.To explain how this

/**
* Update counter task. Update from start value by delta value.
*/
private static class Range implements Comparable<Range> {
    /** */
    private long start;

    /** */
    private long delta;

    /** {@inheritDoc} */
    @Override public int compareTo(@NotNull Range r) {
        return Long.compare(this.start, r.start);
    }

}

A range represents a sequence of updates, for example (5, 3) means three updates with number 6, 7, 8. We will use this notation again later.

Out of order updates range is held in the sequence only if an update with lower range is missing. For example, LWM=5, HWM=9, seq=(6,2) means updates 7, 8 were applied out of order and updates 6, 9 are still not applied.

Note that there is an important invariant which can be derived from the statements above:

HWM >= LWM

Update counter flow

The flow is closely bound to transactions protocol. Counters are modified during stages of two-phase commit (2PC) protocol used by Ignite to ensure distributed consistency.

2PC protocol variation used in Ignite consists of map, optional remap, lock, prepare and commit phases.

We will use an example to illustrate the flow: is applied to achive consistency let us consider the example.

Suppose we have 3 server nodes and 1 client node in the topology and topology version is N.

Two threads on client start two transaction updating keys from start two pessimistic (https://www.gridgain.com/docs/latest/developers-guide/key-value-api/transactions#pessimistic-transactions) transactions concurrently updating keys belonging to the single partition p. Backups count is equal to 2, so the total number of owners is 3.

Tx1 enlists and updates keys k1, k2, k3, ; Tx2 enlists and updates keys k4, k5:

Ignite client = ...

...

.

P(k1) = P(k2) = P(k3) = P(k4) = P(k5) = p.

F(k1) = F(k2) = F(k3) = F(k4) = F(k5) = s1(Primary), s2(Backup1), s3(Backup2)

Ignite client = startClient();

IgniteCache<Integer, Integer> cache = client.cache(name);

Thread 1:

try(Transaction tx1 = client.transactions().txStart())

...

 {

      cache.putAll(Map.of(k1,

...

 v1, k2,

...

 v2, k3, v3)

...

);

      tx1.commit();

...

}

Thread 2:

try(Transaction tx2 = client.transactions().txStart())

...

 {

      cache.putAll(Map.of(k4,

...

      cache.put (k5, v5);

...

 v4, k5, v4);

      tx2.commit();

...

}

To deeper understand counter flow it is necessary to explain how ditributed transactions are working.

Txs use 2PC protocol to consistently update all participating nodes.

The first step (near map) on the near(originating) node is to calculate the map of this transaction - for each partition p a set of involved primary nodes using the affinity function F(p) .

Figure1 below shows the flow with the case of backup reordering, where tx with higher update counters is applied before the transaction with lower update counters on backup.

Image Added

Near map is a mapping of partitions to primary nodes. In the above example, each transaction will have only one node s1 in the near map.

When near map is ready for each key enlisted in the transaction, a lock request is issued which acquires exclusive lock on each key (for optimistic transactions locks are acquired during commit but principles are the sameFor this partition on this topology version a lock request is sent that executes the lock (k) operation, the semantics of which is similar to acquiring exclusuve lock on given key (footnote: this is true for pessimistic transactions).

It may happen that on the primary node, at the time of request processing, the topology version N + 1 is already relevant or in the process of changing to what the version.

If the next version is not compatible with the previous version (having the same partition assignment), a response is sent to the near node indicating that a more recent version of the topology is acutal available and the transaction should retry on the next version. This process is called remap.

If the lock is successfully acquired, the prepare phase begins. . This also globally locks a topology version. A new topology cannot be set until all transactions that started on the current topology have finished. This is handled by the PME protocol.

Each locked First, each enlisted key is assigned the update sequence number ,  generated by using the next value of HWM counter field. If multiple keys are locked in the same partition, a range will be reserved.

For each primary partition, a DHT map is calculated. A DHT map is a mapping of partitions to backup nodes. In the example, each transaction will have two nodes in the DHT map: s2 and s3.

When the DHT map is ready, the primary node sends prepare requests Then primary node builds a backups map (dht map) and sends prepare requests to the backup nodes, if any, containing enlisted keys and their update numberscounters to the corresponding backup nodes.

Thus, update numbers counters are synchronized between primaries and backups - assigned to primaries, then transferred to backups.

If all backup nodes answered positivelyreturn a positive response, then the transaction goes into the PREPARED state. In this state, it the transaction is ready for commit.

During commit, all transactional changes are applied to the WAL first, and ONLY AFTER WHAT the LWM is increased next. At this point, LWM catches up with HWM.

Because transaction commits can reorder, it's possible to have some updates applied out of order, as shown in the example above. The field oomQueue out of order updates sequence is used for tracking such updates.

The figure 3 shows transaction map for tx1:

TODO figure 3

Lets return to our example and assume all locks are aquired and transactions are started to prepare almost simultaneously.

The diagram shows counter state updates for primary and backup during tx processing by two transactions with reordered prepare/commit.

TODO pic

HWM is increment during tx prepare phase on the primary node to ensure the monotonicity of the counter and invariant HWM >= LWM holding.

Let us consider in more detail how counters are used to restore consistency (counters rebalance [2]) when outdated node joins topology.

them.

Let's imaging that out of order updates are not tracked and LWM doesn't exist. In this case, if the primary node fails before applying the update from Tx1, we will end up with update counter=5 and will lose missed updates after the node rejoins to the topology because rebalancing will not start due to equal counters.

Restoring consistency

The LWM value is used to calculate missing data when an outdated node joins the topology. In this scenario, counter based rebalancing is used to achieve consistency.

The On joining node sends LWM for each partition as a part of the partition map exchange protocol. Coordinator The coordinator collects all LWMs and determines nodes with highest LWM , say maxLWMfor each partition. These nodes are elected as data suppliers for a given partition.

If the coordinator detects a lagging node, it sends a partition map with the partition state changed to MOVING, triggering a rebalance on correspondingthe joining node. The missing number of update on updates for each node partition is between it's LWM and maxLWM

The simplified rebalancing flow is illustratred on fugure 3.

calculated as the difference between the highest known LWM and LWM from the joining node.

Figure 2 illustrates the simplified rebalancing flow of a node join with an outdated partition id=0.

Image AddedTODO fig. 3

Update counters are used to determine the state when the one partition copy is behind the other(s)others.

It is only safe to compare LWMs because of guarantee of presence of sequential updates up to LWMall updates with counters below LWM are guaranteed to exist.

Thus, it is very important to correctly track LWMs since thier their values are used to calculate when rebalancing is necessary to launch to restore the integrity of partitions [2].

As already was mentioned, LWM could safely be incremented it is safe to increment LWM only if the update is written to the WAL. Otherwise, a situation is possible when where the counter has been increased , but there is no corresponding durable update.

Crash recovery

There is are no guarantee that this update will be completed, for guarantees that updates with already assigned counters will ever be applied.For example, if during the dht prepare transaction phase some mapped nodes will exit, then the transaction may will be rolled back.
When a primary node fails under load situations may arise where may be permanent gaps in the updates on backup nodes in the event of transaction reordering.

1.

Transactions that started later with a higher counter value were prepared before the transaction with a lower counter value were able to be prepared on at least one backup.
In this case, the gaps will be closed on node left PME, and similarly closed in WAL using RollbackRecord if persistence is enabled.

; or it is also possible for a node to crash during commit.

This causes gaps in the update sequence. Several additional mechanisms are introduced to close such gaps.

Consider figure 3:

Image Added

In the scenario on figure 3, Tx2 is committed before Tx1 had the chance to prepare, and the primary node fails.

As a part of the PME protocol, when a node leaves, all existing gaps are removed after the completion of all active transactions. This happens in org.This scenario is handled by org.apache.ignite.internal.processors.cache.PartitionUpdateCounter#finalizeUpdateCounters

2.

Transactions that started later with a higher counter value were prepared before the transaction with a lower counter value were able to be prepared only on some backups, but not all. This means some of backups never seen a counter update.

In this case, the gaps will be closed by special king of message PartitionUpdateCountersRequest on tx rollback, and similarly closed in WAL using RollbackRecord if persistence is enabled.

TODO fig

Switching primaries

.

Consider figure 4:

Image Added

In the scenario in figure 4, the transaction is prepared only on one backup and the primary node fails. A special partition counters update request is used to sync counters with remaining backup node.

Importance of atomic primaries switching

As shown in figure 4, when the primary node failsIf the primary node fell out, then one of the backups becomes must become the new node, and due to the PME protocol atomic switching to the new primary takes place, where hwm becomes equal to the last known lwm for partition (see Figure 2)primary according to the affinity assignment for the next topology. This happens during the partition map exchange.

During PME on counter exchange phase, a new primary adjusts its HWM to the max LWM value for each cache partition. This guarantees monotonous growth of HWM in the grid and valid generation of counters by new primaries.

It is important to ensure the atomicity of when switching the node’s primaries for partitioning in node entry or exit scenarios, otherwise primary node for partition on all grid nodes.  Otherwise, the HWM >= LWM invariant will break, which ensures that if at least one stable copy is available, everyone else can restore its integrity from it. This is achieved due to the fact that transactions on the previous topology are completed before switching (as a phase

Atomic switching is one of the PME protocol ) to a new one and the situation when two primaries of a node simultaneously exist in a grid for one partition is impossible.guaranties, making it impossible for two different grid nodes to use different primary node for mapping.

The same issue exists when a node joinsIn the event of a node crash under load after restart, local recovery from WAL is started. At the same time, part of the updates could be skipped. At the start, during WAL recovery, the correct lwm will be determined due to the fact that we store gaps in the counter and is used for rebalance. Rebalance will close all gaps, and when you enter the node, you can clear them.