Two Section Commit

Locks and Transaction Isolation

The requests additionally take a lock on the keys.
Significantly, the get requests take a learn lock and
the put requests take a write lock. The learn locks are taken because the
values are learn.

class TransactionalKVStore…

  public CompletableFuture<String> get(TransactionRef txn, String key) {
      CompletableFuture<TransactionRef> lockFuture
              = lockManager.purchase(txn, key, LockMode.READ);
      return lockFuture.thenApply(transactionRef -> {
          getOrCreateTransactionState(transactionRef);
          return kv.get(key);
      });
  }

  synchronized TransactionState getOrCreateTransactionState(TransactionRef txnRef) {
      TransactionState state = this.ongoingTransactions.get(txnRef);
      if (state == null) {
          state = new TransactionState();
          this.ongoingTransactions.put(txnRef, state);
      }
      return state;
  }

The write locks may be taken solely when the transaction is about to commit
and the values are to be made seen in the important thing worth retailer. Till then, the
cluster node can simply observe the modified values as pending operations.

Delaying locking decreases the probabilities of conflicting transactions.

class TransactionalKVStore…

  public void put(TransactionRef transactionRef, String key, String worth) {
      TransactionState state = getOrCreateTransactionState(transactionRef);
      state.addPendingUpdates(key, worth);
  }

You will need to be aware that the locks are lengthy lived and never launched
when the request completes. They’re launched solely when the transaction commits.
This system of holding locks during the transaction
and releasing them solely when the transaction commits or rolls again is
known as two-phase-locking.
Two-phase locking is important in offering the serializable isolation stage.
Serializable which means that the consequences of the transactions are seen as
if they’re executed one after the other.

  WaitPolicy waitPolicy;

public enum WaitPolicy {
    WoundWait,
    WaitDie,
    Error
}

  Queue<LockRequest> waitQueue = new LinkedList<>();
  Listing<TransactionRef> homeowners = new ArrayList<>();
  LockMode lockMode;

  public synchronized CompletableFuture<TransactionRef> purchase(TransactionRef txn, String key, LockMode lockMode) {
      return purchase(txn, key, lockMode, new CompletableFuture<>());
  }

  CompletableFuture<TransactionRef> purchase(TransactionRef txnRef,
                                            String key,
                                            LockMode askedLockMode,
                                            CompletableFuture<TransactionRef> lockFuture) {
      Lock lock = getOrCreateLock(key);

      logger.debug("buying lock for = " + txnRef + " on key = " + key + " with lock mode = " + askedLockMode);
      if (lock.isCompatible(txnRef, askedLockMode)) {
          lock.addOwner(txnRef, askedLockMode);
          lockFuture.full(txnRef);
          logger.debug("acquired lock for = " + txnRef);
          return lockFuture;
      }

  public boolean isCompatible(TransactionRef txnRef, LockMode lockMode) {
      if(hasOwner()) 
      return true;
  }

  non-public CompletableFuture<TransactionRef> handleConflict(Lock lock,
                                                           TransactionRef txnRef,
                                                           String key,
                                                           LockMode askedLockMode,
                                                           CompletableFuture<TransactionRef> lockFuture) {
      change (waitPolicy) {
          case Error: {
              lockFuture.completeExceptionally(new WriteConflictException(txnRef, key, lock.homeowners));
              return lockFuture;
          }
          case WoundWait: {
              return lock.woundWait(txnRef, key, askedLockMode, lockFuture, this);
          }
          case WaitDie: {
              return lock.waitDie(txnRef, key, askedLockMode, lockFuture, this);
          }
      }
      throw new IllegalArgumentException("Unknown waitPolicy " + waitPolicy);
  }

  boolean after(TransactionRef otherTransactionRef) {
      return this.startTimestamp > otherTransactionRef.startTimestamp;
  }

  non-public int requestId;
  public MonotonicId start() {
      return new MonotonicId(requestId++, config.getServerId());
  }

  public class MonotonicId implements Comparable<MonotonicId> {
      public int requestId;
      int serverId;
  
      public MonotonicId(int requestId, int serverId) {
          this.serverId = serverId;
          this.requestId = requestId;
      }
  
      public static MonotonicId empty() {
          return new MonotonicId(-1, -1);
      }
  
      public boolean isAfter(MonotonicId different) {
          if (this.requestId == different.requestId) {
              return this.serverId > different.serverId;
          }
          return this.requestId > different.requestId;
      }

  non-public void beginTransaction(String key) {
      if (coordinator == null) {
          coordinator = replicaMapper.serverFor(key);
          MonotonicId transactionId = coordinator.start();
          transactionRef = new TransactionRef(transactionId, clock.nanoTime());
      }
  }

  public void incrementAge(SystemClock clock) {
      age = clock.nanoTime() - startTimestamp;
  }

  public boolean isAfter(TransactionRef different) {
       return age == different.age?
                  this.id.isAfter(different.id)
                  :this.age > different.age;
  }

  public CompletableFuture<TransactionRef> woundWait(TransactionRef txnRef,
                                                     String key,
                                                     LockMode askedLockMode,
                                                     CompletableFuture<TransactionRef> lockFuture,
                                                     LockManager lockManager) {

      if (allOwningTransactionsStartedAfter(txnRef) && !anyOwnerIsPrepared(lockManager)) {
          abortAllOwners(lockManager, key, txnRef);
          return lockManager.purchase(txnRef, key, askedLockMode, lockFuture);
      }

      LockRequest lockRequest = new LockRequest(txnRef, key, askedLockMode, lockFuture);
      lockManager.logger.debug("Including to attend queue = " + lockRequest);
      addToWaitQueue(lockRequest);
      return lockFuture;
  }

  non-public boolean allOwningTransactionsStartedAfter(TransactionRef txn) {
      return homeowners.stream().filter(o -> !o.equals(txn)).allMatch(proprietor -> proprietor.after(txn));
  }

  public CompletableFuture<TransactionRef> waitDie(TransactionRef txnRef,
                                                   String key,
                                                   LockMode askedLockMode,
                                                   CompletableFuture<TransactionRef> lockFuture,
                                                   LockManager lockManager) {
      if (allOwningTransactionsStartedAfter(txnRef)) {
          addToWaitQueue(new LockRequest(txnRef, key, askedLockMode, lockFuture));
          return lockFuture;
      }

      lockManager.abort(txnRef, key);
      lockFuture.completeExceptionally(new WriteConflictException(txnRef, key, homeowners));
      return lockFuture;
  }

  non-public void launch(TransactionRef txn, String key) {
      Elective<Lock> lock = getLock(key);
      lock.ifPresent(l -> {
          l.launch(txn, this);
      });
  }

  public void launch(TransactionRef txn, LockManager lockManager) {
      removeOwner(txn);
      if (hasWaiters()) {
          LockRequest lockRequest = getFirst(lockManager.waitPolicy);
          lockManager.purchase(lockRequest.txn, lockRequest.key, lockRequest.lockMode, lockRequest.future);
      }
  }

Commit and Rollback

As soon as the consumer efficiently reads with out going through any conflicts and
writes all the important thing values, it initiates the commit request by sending
a commit request to the coordinator.

class TransactionClient…

  public CompletableFuture<Boolean> commit() {
      return coordinator.commit(transactionRef);
  }

The transaction coordinator data the state of the transaction as
making ready to commit. The coordinator implements the commit dealing with in
two phases.

• It first sends the put together request to every of the contributors.
• As soon as it receives a profitable response from all of the contributors,
  the coordinator marks the transaction as ready to finish.
  Then it sends the commit request to all of the contributors.

class TransactionCoordinator…

  public CompletableFuture<Boolean> commit(TransactionRef transactionRef)  {
      TransactionMetadata metadata = transactions.get(transactionRef);
      metadata.markPreparingToCommit(transactionLog);
      Listing<CompletableFuture<Boolean>> allPrepared = sendPrepareRequestToParticipants(transactionRef);
      CompletableFuture<Listing<Boolean>> futureList = sequence(allPrepared);
      return futureList.thenApply(end result -> {
          if (!end result.stream().allMatch(r -> r)) {
              logger.data("Rolling again = " + transactionRef);
              rollback(transactionRef);
              return false;
          }
          metadata.markPrepared(transactionLog);
          sendCommitMessageToParticipants(transactionRef);
          metadata.markCommitComplete(transactionLog);
          return true;
      });
  }

  public Listing<CompletableFuture<Boolean>> sendPrepareRequestToParticipants(TransactionRef transactionRef)  {
      TransactionMetadata transactionMetadata = transactions.get(transactionRef);
      var transactionParticipants = getParticipants(transactionMetadata.getParticipatingKeys());
      return transactionParticipants.keySet()
              .stream()
              .map(server -> server.handlePrepare(transactionRef))
              .accumulate(Collectors.toList());
  }

  non-public void sendCommitMessageToParticipants(TransactionRef transactionRef) {
      TransactionMetadata transactionMetadata = transactions.get(transactionRef);
      var participantsForKeys = getParticipants(transactionMetadata.getParticipatingKeys());
      participantsForKeys.keySet().stream()
              .forEach(kvStore -> {
                  Listing<String> keys = participantsForKeys.get(kvStore);
                  kvStore.handleCommit(transactionRef, keys);
              });
  }

  non-public Map<TransactionalKVStore, Listing<String>> getParticipants(Listing<String> participatingKeys) {
      return participatingKeys.stream()
              .map(ok -> Pair.of(serverFor(ok), ok))
              .accumulate(Collectors.groupingBy(Pair::getKey, Collectors.mapping(Pair::getValue, Collectors.toList())));
  }

The cluster node receiving the put together requests do two issues:

• It tries to seize the write locks for all the keys.
• As soon as profitable, it writes all the modifications to the write-ahead log.

If it could actually efficiently do these,
it could actually assure that there aren’t any conflicting transactions,
and even within the case of a crash the cluster node can recuperate all of the
required state to finish the transaction.

class TransactionalKVStore…

  public synchronized CompletableFuture<Boolean> handlePrepare(TransactionRef txn) {
      attempt {
          TransactionState state = getTransactionState(txn);
          if (state.isPrepared()) {
              return CompletableFuture.completedFuture(true); //already ready.
          }

          if (state.isAborted()) {
              return CompletableFuture.completedFuture(false); //aborted by one other transaction.
          }

          Elective<Map<String, String>> pendingUpdates = state.getPendingUpdates();
          CompletableFuture<Boolean> prepareFuture = prepareUpdates(txn, pendingUpdates);
          return prepareFuture.thenApply(ignored -> {
              Map<String, Lock> locksHeldByTxn = lockManager.getAllLocksFor(txn);
              state.markPrepared();
              writeToWAL(new TransactionMarker(txn, locksHeldByTxn, TransactionStatus.PREPARED));
              return true;
          });

      } catch (TransactionException| WriteConflictException e) {
          logger.error(e);
      }
      return CompletableFuture.completedFuture(false);
  }

  non-public CompletableFuture<Boolean> prepareUpdates(TransactionRef txn, Elective<Map<String, String>> pendingUpdates)  {
      if (pendingUpdates.isPresent()) {
          Map<String, String> pendingKVs = pendingUpdates.get();
          CompletableFuture<Listing<TransactionRef>> lockFuture = acquireLocks(txn, pendingKVs.keySet());
          return lockFuture.thenApply(ignored -> {
              writeToWAL(txn, pendingKVs);
              return true;
          });
      }
      return CompletableFuture.completedFuture(true);
  }

  TransactionState getTransactionState(TransactionRef txnRef) {
      return ongoingTransactions.get(txnRef);
  }

  non-public void writeToWAL(TransactionRef txn, Map<String, String> pendingUpdates) {
     for (String key : pendingUpdates.keySet()) {
          String worth = pendingUpdates.get(key);
          wal.writeEntry(new SetValueCommand(txn, key, worth).serialize());
      }
  }

  non-public CompletableFuture<Listing<TransactionRef>> acquireLocks(TransactionRef txn, Set<String> keys) {
      Listing<CompletableFuture<TransactionRef>> lockFutures = new ArrayList<>();
      for (String key : keys) {
          CompletableFuture<TransactionRef> lockFuture = lockManager.purchase(txn, key, LockMode.READWRITE);
          lockFutures.add(lockFuture);
      }
      return sequence(lockFutures);
  }

When the cluster node receives the commit message from the coordinator,
it’s protected to make the key-value modifications seen.
The cluster node does three issues whereas committing the modifications:

• It marks the transaction as dedicated. Ought to the cluster node fail at this level,
  it is aware of the end result of the transaction, and may repeat the next steps.
• It applies all of the modifications to the key-value storage
• It releases all of the acquired locks.

class TransactionalKVStore…

  public synchronized void handleCommit(TransactionRef transactionRef, Listing<String> keys) {
      if (!ongoingTransactions.containsKey(transactionRef)) {
          return; //this can be a no-op. Already dedicated.
      }

      if (!lockManager.hasLocksFor(transactionRef, keys)) {
          throw new IllegalStateException("Transaction " + transactionRef + " ought to maintain all of the required locks for keys " + keys);
      }

      writeToWAL(new TransactionMarker(transactionRef, TransactionStatus.COMMITTED, keys));

      applyPendingUpdates(transactionRef);

      releaseLocks(transactionRef, keys);
  }

  non-public void removeTransactionState(TransactionRef txnRef) {
      ongoingTransactions.take away(txnRef);
  }


  non-public void applyPendingUpdates(TransactionRef txnRef) {
      TransactionState state = getTransactionState(txnRef);
      Elective<Map<String, String>> pendingUpdates = state.getPendingUpdates();
      apply(txnRef, pendingUpdates);
  }

  non-public void apply(TransactionRef txnRef, Elective<Map<String, String>> pendingUpdates) {
      if (pendingUpdates.isPresent()) {
          Map<String, String> pendingKv = pendingUpdates.get();
          apply(pendingKv);
      }
      removeTransactionState(txnRef);
  }

  non-public void apply(Map<String, String> pendingKv) {
      for (String key : pendingKv.keySet()) {
          String worth = pendingKv.get(key);
          kv.put(key, worth);
      }
  }
  non-public void releaseLocks(TransactionRef txn, Listing<String> keys) {
          lockManager.launch(txn, keys);
  }

  non-public Lengthy writeToWAL(TransactionMarker transactionMarker) {
     return wal.writeEntry(transactionMarker.serialize());
  }

The rollback is carried out in the same approach. If there’s any failure,
the consumer communicates with the coordinator to rollback the transaction.

class TransactionClient…

  public void rollback() {
      coordinator.rollback(transactionRef);
  }

The transaction coordinator data the state of the transaction as making ready
to rollback. Then it forwards the rollback request to all the servers
which saved the values for the given transaction.
As soon as all the requests are profitable, the coordinator marks the transaction
rollback as full.In case the coordinator crashes after the transaction
is marked as ‘ready to rollback’, it could actually carry on sending the rollback
messages to all of the taking part cluster nodes.

class TransactionCoordinator…

  public void rollback(TransactionRef transactionRef) {
      transactions.get(transactionRef).markPrepareToRollback(this.transactionLog);

      sendRollbackMessageToParticipants(transactionRef);

      transactions.get(transactionRef).markRollbackComplete(this.transactionLog);
  }

  non-public void sendRollbackMessageToParticipants(TransactionRef transactionRef) {
      TransactionMetadata transactionMetadata = transactions.get(transactionRef);
      var contributors = getParticipants(transactionMetadata.getParticipatingKeys());
      for (TransactionalKVStore kvStore : contributors.keySet()) {
          Listing<String> keys = contributors.get(kvStore);
          kvStore.handleRollback(transactionMetadata.getTxn(), keys);
      }
  }

The cluster nodes receiving the rollback request does three issues:

• It data the state of the transaction as rolled again within the write-ahead log.
• It discards the transaction state.
• It releases all the locks

class TransactionalKVStore…

  public synchronized void handleRollback(TransactionRef transactionRef, Listing<String> keys) {
      if (!ongoingTransactions.containsKey(transactionRef)) {
          return; //no-op. Already rolled again.
      }
      writeToWAL(new TransactionMarker(transactionRef, TransactionStatus.ROLLED_BACK, keys));
      this.ongoingTransactions.take away(transactionRef);
      this.lockManager.launch(transactionRef, keys);
  }

An Instance State of affairs

Atomic Writes

Contemplate the next situation. Paula Blue has a truck and Steven Inexperienced
has a backhoe.
The supply and the reserving standing of the truck and the backhoe
are saved on a distributed key-value retailer.
Relying on how the keys are mapped to servers,
Blue’s truck and Inexperienced’s backhoe bookings are saved on
separate cluster nodes.
Alice is making an attempt to ebook a truck
and backhoe for the development work she is planning to begin on a Monday.
She wants each the truck and the backhoe to be obtainable.

The reserving situation occurs as follows.

Alice checks the provision of Blue’s truck and Inexperienced’s backhoe.
by studying the keys ‘truck_booking_monday’ and ‘backhoe_booking_monday’

If the values are empty, the reserving is free.
She reserves the truck and the backhoe.
It is vital that each the values are set atomically.
If there’s any failure, then not one of the values is about.

The commit occurs in two phases. The primary server Alice
contacts acts because the coordinator and executes the 2 phases.

The coordinator is a separate participant within the
protocol, and is proven that approach on the sequence diagram. Nevertheless
often one of many servers (Blue or Inexperienced) acts as
the coordinator, thus enjoying two roles within the interplay.

Conflicting Transactions

Contemplate a situation the place one other individual, Bob, can be making an attempt to ebook a
truck and backhoe for building work on the identical Monday.

The reserving situation occurs as follows:

• Each Alice and Bob learn the keys ‘truck_booking_monday’
  and ‘backhoe_booking_monday’
• Each see that the values are empty, which means the reserving is free.
• Each attempt to ebook the truck and the backhoe.

The expectation is that, solely Alice or Bob, ought to have the ability to ebook,
as a result of the transactions are conflicting.
In case of errors, the entire movement must be retried and hopefully,
one will go forward with the reserving.
However in no state of affairs, ought to reserving be accomplished partially.
Both each bookings must be accomplished or neither is completed.

To test the provision, each Alice and Bob begin a transaction
and phone Blue and Inexperienced’s servers respectively to test for the provision.
Blue holds a learn lock for the important thing “truck_booking_on_monday” and
Inexperienced holds a learn lock for the important thing “backhoe_booking_on_monday”.
As a result of learn locks are shared, each Alice and Bob can learn the values.

Alice and Bob see that each the bookings can be found on Monday.
So that they reserve by sending the put requests to servers.
Each the servers maintain the put requests within the momentary storage.

When Alice and Bob resolve to commit the transactions-
assuming that Blue acts as a coordinator- it triggers the two-phase
commit protocol and sends the put together requests to itself and Inexperienced.

For Alice’s request it tries to seize a write lock for the important thing ‘truck_booking_on_monday’, which
it can’t get, as a result of there’s a conflicting learn lock grabbed by
one other transaction. So Alice’s transaction fails within the put together part.
The identical factor occurs with Bob’s request.

Transactions may be retried with a retry loop as follows:

class TransactionExecutor…

  public boolean executeWithRetry(Operate<TransactionClient, Boolean> txnMethod, ReplicaMapper replicaMapper, SystemClock systemClock) {
      for (int try = 1; try <= maxRetries; try++) {
          TransactionClient consumer = new TransactionClient(replicaMapper, systemClock);
          attempt {
              boolean checkPassed = txnMethod.apply(consumer);
              Boolean successfullyCommitted = consumer.commit().get();
              return checkPassed && successfullyCommitted;
          } catch (Exception e) {
              logger.error("Write battle detected whereas executing." + consumer.transactionRef + " Retrying try " + try);
              consumer.rollback();
              randomWait(); //watch for random interval
          }

      }
      return false;
  }

The instance reserving code for Alice and Bob will look as follows:

class TransactionalKVStoreTest…

  @Take a look at
  public void retryWhenConflict()  (!aliceTxn.isSuccess() && bobTxn.isSuccess()), "ready for one txn to finish", Period.ofSeconds(50));
  

  non-public TransactionExecutor bookTransactionally(Listing<TransactionalKVStore> allServers, String consumer, SystemClock systemClock) {
      Listing<String> bookingKeys = Arrays.asList("truck_booking_on_monday", "backhoe_booking_on_monday");
      TransactionExecutor t1 = new TransactionExecutor(allServers);
      t1.executeAsyncWithRetry(txnClient -> {
          if (txnClient.isAvailable(bookingKeys)) {
              txnClient.reserve(bookingKeys, consumer);
              return true;
          }
          return false;
      }, systemClock);
      return t1;
  }

On this case one of many transactions will finally succeed and
the opposite will again out.

Whereas it is rather simple to implement, with Error WaitPolicy ,
there will probably be a number of transaction restarts,lowering the general
throughput.
As defined within the above part, if Wound-Wait coverage is used
it can have fewer transaction restarts. Within the above instance,
just one transaction will presumably restart as a substitute of each restarting
in case of conflicts.

Utilizing Versioned Worth

It is extremely constraining to have conflicts for all of the learn and write
operations, significantly so when the transactions may be read-only.
It’s optimum if read-only transactions can work with out holding any
locks and nonetheless assure that the values learn in a transaction
don’t change with a concurrent read-write transaction.

Knowledge-stores usually retailer a number of variations of the values,
as described in Versioned Worth.
The model used is the timestamp following Lamport Clock.
Principally a Hybrid Clock is utilized in databases like
MongoDB or CockroachDB.
To make use of it with the two-phase commit protocol, the trick is that each server
taking part within the transaction sends the timestamp it could actually write the
values at, as response to the put together request.
The coordinator chooses the utmost of those timestamps as a
commit timestamp and sends it together with the worth.
The taking part servers then save the worth on the commit timestamp.
This permits read-only requests to be executed with out holding locks,
as a result of it is assured that the worth written at a specific timestamp
isn’t going to vary.

Contemplate a easy instance as follows. Philip is working a report back to learn
all the bookings that occurred till timestamp 2. If it’s a long-running
operation holding a lock, Alice, who’s making an attempt to ebook a truck, will probably be blocked
till Philip’s work completes. With Versioned Worth
Philip’s get requests, that are a part of a read-only operation, can proceed
at timestamp 2, whereas Alice’s reserving continues at timestamp 4.

Be aware that learn requests that are a part of a read-write transaction,
nonetheless want to carry a lock.

The instance code with Lamport Clock appears to be like as follows:

class MvccTransactionalKVStore…

  public String readOnlyGet(String key, lengthy readTimestamp) {
      adjustServerTimestamp(readTimestamp);
      return kv.get(new VersionedKey(key, readTimestamp));
  }

  public CompletableFuture<String> get(TransactionRef txn, String key, lengthy readTimestamp) {
      adjustServerTimestamp(readTimestamp);
      CompletableFuture<TransactionRef> lockFuture = lockManager.purchase(txn, key, LockMode.READ);
      return lockFuture.thenApply(transactionRef -> {
          getOrCreateTransactionState(transactionRef);
          return kv.get(key);
      });
  }

  non-public void adjustServerTimestamp(lengthy readTimestamp) {
      this.timestamp = readTimestamp > this.timestamp ? readTimestamp:timestamp;
  }

  public void put(TransactionRef txnId, String key, String worth) {
      timestamp = timestamp + 1;
      TransactionState transactionState = getOrCreateTransactionState(txnId);
      transactionState.addPendingUpdates(key, worth);
  }

class MvccTransactionalKVStore…

  non-public lengthy put together(TransactionRef txn, Elective<Map<String, String>> pendingUpdates) throws WriteConflictException, IOException {
      if (pendingUpdates.isPresent()) {
          Map<String, String> pendingKVs = pendingUpdates.get();

          acquireLocks(txn, pendingKVs);

          timestamp = timestamp + 1; //increment the timestamp for write operation.

          writeToWAL(txn, pendingKVs, timestamp);
       }
      return timestamp;
  }

class MvccTransactionCoordinator…

  public lengthy commit(TransactionRef txn) {
          lengthy commitTimestamp = put together(txn);

          TransactionMetadata transactionMetadata = transactions.get(txn);
          transactionMetadata.markPreparedToCommit(commitTimestamp, this.transactionLog);

          sendCommitMessageToAllTheServers(txn, commitTimestamp, transactionMetadata.getParticipatingKeys());

          transactionMetadata.markCommitComplete(transactionLog);

          return commitTimestamp;
  }


  public lengthy put together(TransactionRef txn) throws WriteConflictException {
      TransactionMetadata transactionMetadata = transactions.get(txn);
      Map<MvccTransactionalKVStore, Listing<String>> keysToServers = getParticipants(transactionMetadata.getParticipatingKeys());
      Listing<Lengthy> prepareTimestamps = new ArrayList<>();
      for (MvccTransactionalKVStore retailer : keysToServers.keySet()) {
          Listing<String> keys = keysToServers.get(retailer);
          lengthy prepareTimestamp = retailer.put together(txn, keys);
          prepareTimestamps.add(prepareTimestamp);
      }
      return prepareTimestamps.stream().max(Lengthy::examine).orElse(txn.getStartTimestamp());
  }

All of the taking part cluster nodes then retailer the key-values on the
commit timestamp.

class MvccTransactionalKVStore…

  public void commit(TransactionRef txn, Listing<String> keys, lengthy commitTimestamp) {
      if (!lockManager.hasLocksFor(txn, keys)) {
          throw new IllegalStateException("Transaction ought to maintain all of the required locks");
      }

      adjustServerTimestamp(commitTimestamp);

      applyPendingOperations(txn, commitTimestamp);

      lockManager.launch(txn, keys);

      logTransactionMarker(new TransactionMarker(txn, TransactionStatus.COMMITTED, commitTimestamp, keys, Collections.EMPTY_MAP));
  }

  non-public void applyPendingOperations(TransactionRef txnId, lengthy commitTimestamp) {
      Elective<TransactionState> transactionState = getTransactionState(txnId);
      if (transactionState.isPresent()) {
          TransactionState t = transactionState.get();
          Elective<Map<String, String>> pendingUpdates = t.getPendingUpdates();
          apply(txnId, pendingUpdates, commitTimestamp);
      }
  }

  non-public void apply(TransactionRef txnId, Elective<Map<String, String>> pendingUpdates, lengthy commitTimestamp) {
      if (pendingUpdates.isPresent()) {
          Map<String, String> pendingKv = pendingUpdates.get();
          apply(pendingKv, commitTimestamp);
      }
      ongoingTransactions.take away(txnId);
  }


  non-public void apply(Map<String, String> pendingKv, lengthy commitTimestamp) {
      for (String key : pendingKv.keySet()) {
          String worth = pendingKv.get(key);
          kv.put(new VersionedKey(key, commitTimestamp), worth);
      }
  }

Utilizing Replicated Log

To enhance fault tolerance cluster nodes use Replicated Log.
The coordinator makes use of Replicated Log to retailer the transaction log entries.

Contemplating the instance of Alice and Bob within the above part,
the Blue servers will probably be a bunch of servers, so are the Inexperienced servers.
All of the reserving knowledge will probably be replicated throughout a set of servers.
Every request which is a part of the two-phase commit goes to the chief
of the server group. The replication is carried out utilizing
Replicated Log.

The consumer communicates with the chief of every server group.
The replication is important solely when the consumer decides to commit the
transaction, so it occurs as a part of the put together request.

The coordinator replicates each state change to replicated log as nicely.

In a distributed datastore, every cluster node handles a number of partitions.
A Replicated Log is maintained per partition.
When Raft is used as a part of replication it is generally
known as multi-raft.

Consumer communicates with the chief of every partition taking part in
the transaction.

Failure Dealing with

Two-phase commit protocol closely depends on the coordinator node
to speak the end result of the transaction.
Till the end result of the transaction is understood,
the person cluster nodes can’t permit another transactions
to put in writing to the keys taking part within the pending transaction.
The cluster nodes block till the end result of the transaction is understood.
This places some important necessities on the coordinator

The coordinator wants to recollect the state of the transactions
even in case of a course of crash.

Coordinator makes use of Write-Forward Log to file each replace
to the state of the transaction.
This fashion, when the coordinator crashes and comes again up,
it could actually proceed to work on the transactions that are incomplete.

class TransactionCoordinator…

  public void loadTransactionsFromWAL() throws IOException {
      Listing<WALEntry> walEntries = this.transactionLog.readAll();
      for (WALEntry walEntry : walEntries) {
          TransactionMetadata txnMetadata = (TransactionMetadata) Command.deserialize(new ByteArrayInputStream(walEntry.getData()));
          transactions.put(txnMetadata.getTxn(), txnMetadata);
      }
      startTransactionTimeoutScheduler();
      completePreparedTransactions();
  }
  non-public void completePreparedTransactions() throws IOException {
      Listing<Map.Entry<TransactionRef, TransactionMetadata>> preparedTransactions
              = transactions.entrySet().stream().filter(entry -> entry.getValue().isPrepared()).accumulate(Collectors.toList());
      for (Map.Entry<TransactionRef, TransactionMetadata> preparedTransaction : preparedTransactions) {
          TransactionMetadata txnMetadata = preparedTransaction.getValue();
          sendCommitMessageToParticipants(txnMetadata.getTxn());
      }
  }

The consumer can fail earlier than sending the commit message to the coordinator.

The transaction coordinator tracks when every transaction state was up to date.
If no state replace is acquired in a timeout interval, which is configured,
it triggers a transaction rollback.

class TransactionCoordinator…

  non-public ScheduledThreadPoolExecutor scheduler = new ScheduledThreadPoolExecutor(1);
  non-public ScheduledFuture<?> taskFuture;
  non-public lengthy transactionTimeoutMs = Lengthy.MAX_VALUE; //for now.

  public void startTransactionTimeoutScheduler() {
      taskFuture = scheduler.scheduleAtFixedRate(() -> timeoutTransactions(),
              transactionTimeoutMs,
              transactionTimeoutMs,
              TimeUnit.MILLISECONDS);
  }

  non-public void timeoutTransactions() {
      for (TransactionRef txnRef : transactions.keySet()) {
          TransactionMetadata transactionMetadata = transactions.get(txnRef);
          lengthy now = systemClock.nanoTime();
          if (transactionMetadata.hasTimedOut(now)) {
              sendRollbackMessageToParticipants(transactionMetadata.getTxn());
              transactionMetadata.markRollbackComplete(transactionLog);
          }
      }
  }

Transactions throughout heterogenous programs

The answer outlined right here demonstrates the two-phase commit implementation
in a homogenous system. Homogenous which means all of the cluster nodes are half
of the identical system and retailer similar type of knowledge. For instance
a distributed knowledge retailer like MongoDb or a distributed message dealer
like Kafka.

Traditionally, two-phase commit was largely mentioned within the context of
heterogeneous programs. Most typical utilization of two-phase commit was
with [XA] transactions. Within the J2EE servers, it is rather
frequent to make use of two-phase commit throughout a message dealer and a database.
The commonest utilization sample is when a message must be produced
on a message dealer like ActiveMQ or JMS and a file must be
inserted/up to date in a database.

As seen within the above sections, the fault tolerance of the coordinator
performs a important position in two-phase commit implementation. In case of XA
transactions the coordinator is generally the appliance course of making
the database and message dealer calls. The appliance in most fashionable
situations is a stateless microservice which is working in a containerized
surroundings. It’s not actually an appropriate place to place the accountability
of the coordinator. The coordinator wants to keep up state and recuperate
shortly from failures to commit or rollback, which is tough to
implement on this case.

That is the rationale that whereas XA transactions appear so engaging, they
usually run into points
in observe and are prevented. Within the microservices
world, patterns like [transactional-outbox] are most well-liked over
XA transactions.

Then again most distributed storage programs implement
two-phase commit throughout a set of partitions, and it really works nicely in observe.