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new 40d173c912 IGNITE-27470 Part 7 of AI3 blog series (#305)
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commit 40d173c912f9c72691cd03241f4317a3e62abc5c
Author: jinxxxoid <[email protected]>
AuthorDate: Tue Jan 6 18:58:42 2026 +0400
IGNITE-27470 Part 7 of AI3 blog series (#305)
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+---
+title: "Apache Ignite Architecture Series: Part 7 - MVCC Transactions for
High-Frequency Processing: ACID at Scale"
+author: "Michael Aglietti"
+date: 2026-01-06
+tags:
+ - apache
+ - ignite
+---
+
+p Traditional databases force a choice between fast transactions and
concurrent analytics, but Apache Ignite's universal transaction model delivers
both simultaneously.
+<!-- end -->
+p #[strong The transformation:] Traditional systems force you to choose
between fast operations and consistent data. Apache Ignite eliminates this
choice through universal transaction consistency that works across all APIs.
+p #[strong Business scenario:] Your bank transfer application needs both
instant balance updates for customers and concurrent fraud analytics on live
transaction data. Traditional databases lock accounts during transfers,
blocking fraud detection exactly when you need it most.
+p #[strong The solution:] Universal transaction model ensures balance updates
and fraud queries both get full ACID guarantees while running concurrently
without blocking each other.
+
+br
+h2 Why Traditional Transactions Fail at Scale
+h3 The Blocking Problem
+p #[strong Traditional transaction problems compound at scale:]
+ol
+ li #[strong Locking blocks analytics]: Account transfers lock balances,
preventing concurrent fraud detection
+ li #[strong Eventually consistent risks]: Stale balance data enables
overdrafts during high-frequency transfers
+ li #[strong System coordination complexity]: Multiple databases with
different consistency guarantees create reconciliation problems
+p #[strong Business impact:] Transaction processing and analytical queries
become mutually exclusive during peak load.
+p #[strong Here's what the blocking problem looks like in practice:]
+pre
+ code.
+ // Traditional locking blocks concurrent operations
+ public TransferResult processTransfer(TransferRequest transfer) {
+ // Exclusive locks block all concurrent access
+ return database.withExclusiveLock(transfer.fromAccountId, () -> {
+ Account account = getAccount(transfer.fromAccountId); // Lock
acquired
+
+ if (account.balance >= transfer.amount) {
+ account.balance -= transfer.amount; // Account
locked during update
+ logTransaction(transfer); //
Transaction log locked
+
+ // Problem: All queries blocked during this processing
+ // Fraud detection: BLOCKED (can't read account during
transfer)
+ // Balance inquiry: BLOCKED (can't check balance)
+ // Analytics: BLOCKED (can't read transaction history)
+
+ return TransferResult.SUCCESS;
+ }
+ return TransferResult.INSUFFICIENT_FUNDS;
+ });
+ }
+p #[strong Eventual Consistency Problems:]
+pre
+ code.
+ // Eventually consistent systems create business risks
+ public void processConcurrentTransfers(String accountId) {
+ // Operations see different data states
+ CompletableFuture<Void> transfer1 =
CompletableFuture.runAsync(() -> {
+ Account account = getAccount(accountId); // Version 1: $1,000
balance
+ if (account.balance >= 600) {
+ processTransfer(accountId, 600); // Transfer $600
+ }
+ });
+
+ CompletableFuture<Void> transfer2 =
CompletableFuture.runAsync(() -> {
+ Account account = getAccount(accountId); // Version 1: $1,000
balance (stale)
+ if (account.balance >= 500) {
+ processTransfer(accountId, 500); // Transfer $500
(overdraft!)
+ }
+ });
+
+ CompletableFuture.allOf(transfer1, transfer2).join();
+ // Result: $1,100 transferred from $1,000 account (business error)
+ }
+h3 Business Requirements That Traditional Systems Can't Meet
+p #[strong Banking platform needs:]
+ul
+ li #[strong Transfer processing]: 10,000 transfers/second with ACID
guarantees
+ li #[strong Fraud detection]: Real-time analytics on live transaction data
+ li #[strong Balance inquiries]: Customer balance checks without blocking
transfers
+ li #[strong Compliance reporting]: Regulatory queries on active transaction
streams
+ li #[strong Performance requirement]: All operations sub-millisecond
response time
+
+br
+h2 Apache Ignite Universal Transaction Model
+br
+p #[strong The breakthrough:] Every API provides identical ACID guarantees. No
complexity tiers, no eventual consistency compromises.
+h3 How Universal Transactions Work
+p Apache Ignite combines distributed coordination with concurrent access
control to solve both consistency and performance problems:
+p #[strong Universal benefits:]
+ul
+ li #[strong Same consistency everywhere]: Cache operations, SQL queries, and
compute jobs all provide identical ACID guarantees
+ li #[strong Concurrent access]: Analytics run simultaneously with
transaction processing without blocking
+ li #[strong Distributed safety]: All nodes maintain consistency during
network partitions
+ li #[strong Performance optimized]: Background coordination doesn't block
application operations
+h3 The One Transaction Model Advantage
+p #[strong Here's the unified transaction guarantee that changes everything:]
+pre
+ code.
+ // ALL operations share the same transaction model and ACID guarantees
+ Table accountsTable = client.tables().table("accounts");
+ // Key-value operation - full ACID guarantees
+ Tuple account = accountsTable.keyValueView().get(tx, accountKey);
+ // SQL operation - full ACID guarantees (concurrent fraud detection)
+ ResultSet<SqlRow> fraudCheck = client.sql().execute(tx,
+ "SELECT account_id, COUNT(*) FROM transfers WHERE amount > 5000 GROUP
BY account_id");
+ // Record operation - full ACID guarantees
+ Tuple transfer = accountsTable.recordView().get(tx,
+ Tuple.create().set("transfer_id", transferId));
+ // Compute operation - accesses same transactional data
+ CompletableFuture<String> riskFuture = client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeKey), RiskJob.class, tradeId);
+ // Every operation: same consistency model, same isolation level, same
durability
+p #[strong Universal Transaction Benefits:]
+ul
+ li #[strong Same safety everywhere]: Cache operations, SQL queries, and
compute jobs all provide identical ACID safety
+ li #[strong No complexity tiers]: Every operation gets enterprise-grade
transaction safety automatically
+ li #[strong Consistent behavior]: Operations and analytics see identical
consistency guarantees
+ li #[strong Unified recovery]: All operations participate in the same
failure recovery mechanism
+p #[strong The breakthrough]: One transaction model handles everything from
microsecond lookups to complex analytical queries. No complexity tiers, no
eventual consistency compromises.
+h3 How Universal Transactions Handle Mixed Workloads
+p #[strong Apache Ignite solves this with simple API patterns that provide
both consistency and concurrency:]
+pre
+ code.
+ // Read-only transactions for analytics (don't block updates)
+ public FraudAnalytics checkFraudPatterns(String accountId) {
+ // Read-only transaction gets consistent snapshot automatically
+ return ignite.transactions().runInTransaction(tx -> {
+ // All reads see the same consistent point-in-time snapshot
+ Account account = accountTable.get(tx, accountId);
+ List<Transfer> recentTransfers = transferTable.query(tx,
+ "SELECT * FROM transfers WHERE account_id = ? AND timestamp >
?",
+ accountId, Instant.now().minus(Duration.ofHours(24)));
+
+ // Fraud analysis on consistent data
+ return new FraudAnalytics(account, recentTransfers);
+ }, new TransactionOptions().readOnly(true));
+ }
+ // Read-write transactions provide ACID guarantees
+ public TransferResult executeTransfer(TransferRequest transfer) {
+ return ignite.transactions().runInTransaction(tx -> {
+ // Read current account state
+ Account account = accountTable.get(tx, transfer.fromAccountId);
+
+ if (account.balance >= transfer.amount) {
+ // Update account balance atomically
+ account.balance -= transfer.amount;
+ accountTable.put(tx, transfer.fromAccountId, account);
+
+ // Record transfer execution
+ Transfer executedTransfer = new Transfer(transfer,
TransferStatus.COMPLETED);
+ transferTable.put(tx, executedTransfer.transferId,
executedTransfer);
+
+ return TransferResult.SUCCESS;
+ }
+ return TransferResult.INSUFFICIENT_FUNDS;
+ }); // Automatic coordination and commit
+ }
+h3 How Concurrent Access Works
+p Apache Ignite maintains multiple data versions to enable true concurrency:
+p #[strong Concurrent processing benefits:]
+ul
+ li #[strong Analytics queries]: Read from stable data versions without
blocking active transfers
+ li #[strong Transfer updates]: Create new versions while fraud detection
continues accessing stable snapshots
+ li #[strong Memory efficiency]: Automatic cleanup of old versions no longer
needed for consistent reads
+ li #[strong Performance optimization]: Both real-time transfers and
historical analysis operate simultaneously
+p #[strong The breakthrough:] Instead of forcing operations to wait for each
other, multiple versions let both transfer processing and fraud analytics
operate simultaneously against the same logical data.
+
+br
+h2 Performance Under High Load
+h3 Real-World Performance Characteristics
+p #[strong The performance impact becomes clear in a typical trade execution:]
+pre
+ code.
+ // Single trade execution demonstrating performance characteristics
+ public TradeResult processTradeWithMVCC(TradeRequest tradeRequest) {
+ return ignite.transactions().runInTransaction(tx -> {
+ // Read account data (MVCC snapshot access)
+ Account account = accountTable.get(tx, tradeRequest.accountId);
+
+ // Validate and execute trade (MVCC write + RAFT coordination)
+ if (account.balance >= tradeRequest.amount) {
+ account.balance -= tradeRequest.amount;
+ accountTable.put(tx, account.accountId, account);
+
+ Trade trade = new Trade(tradeRequest, TradeStatus.EXECUTED);
+ tradeTable.put(tx, trade.tradeId, trade);
+ return TradeResult.EXECUTED;
+ }
+ return TradeResult.INSUFFICIENT_FUNDS;
+ });
+ }
+p #[strong Performance Outcomes:]
+ul
+ li #[strong Transaction latency]: Sub-millisecond execution with ACID
guarantees
+ li #[strong Concurrent analytics]: Portfolio calculations run simultaneously
without blocking trades
+ li #[strong Throughput capacity]: 12,500 trades/second + 2,500 analytics
queries/second
+ li #[strong Performance interference]: Less than 5% mutual impact during
mixed workloads
+p #[strong The Performance Advantage:] Traditional systems force you to choose
between transaction speed and analytical access. RAFT + MVCC provides both
simultaneously.
+
+br
+h2 Real-World Scenarios
+h3 High-Volume Event Handling
+p #[strong Market Volatility Processing:]
+p During flash crash events or extreme market volatility, trading systems face
spikes to 50,000+ trades per minute while risk monitoring systems need
continuous portfolio analysis. Traditional systems either sacrifice transaction
accuracy or block analytical queries when they're needed most.
+p #[strong RAFT + MVCC Response:]
+ul
+ li #[strong Transaction processing]: Maintains ACID guarantees even during
volume spikes
+ li #[strong Risk monitoring]: Continues real-time portfolio calculations
without blocking trades
+ li #[strong Performance stability]: Consistent sub-millisecond trade
execution regardless of analytical load
+ li #[strong Data accuracy]: No account overdrafts or position errors even
under extreme conditions
+h3 Regulatory Compliance Operations
+p #[strong Live Compliance Reporting:]
+p Regulatory requirements demand real-time transaction monitoring and complex
daily reporting while trading operations continue uninterrupted. Traditional
approaches force compliance teams to wait for low-volume periods or accept
stale data that might miss violations.
+p #[strong The Integration Solution:]
+ul
+ li #[strong Concurrent reporting]: Complex compliance queries process
millions of trades without affecting live operations
+ li #[strong Data consistency]: Point-in-time snapshots ensure accurate
violation detection
+ li #[strong Operational continuity]: Trading performance remains stable
during report generation
+ li #[strong Audit accuracy]: Complete transaction ordering maintained across
distributed nodes
+p #[strong Business Impact:] These scenarios demonstrate why RAFT + MVCC
matters for distributed architectures. It's not just about performance. It's
about maintaining business operations and regulatory compliance during the
high-stress periods when accurate data matters most.
+
+br
+h2 Business Impact of MVCC at Scale
+h3 Financial Risk Mitigation
+p #[strong Trading Firm Benefits:]
+ul
+ li #[strong ACID guarantees]: Zero account overdrafts or position errors
under high load
+ li #[strong Real-time risk management]: Continuous monitoring without
trading performance impact
+ li #[strong Regulatory compliance]: Accurate transaction ordering and
reporting during volatility
+ li #[strong Operational reliability]: Consistent performance during market
stress events
+p #[strong Cost Avoidance Through Accuracy:]
+ul
+ li #[strong Regulatory fines]: Reduced penalties for compliance violations
+ li #[strong Trading errors]: Eliminated losses from account overdrafts
+ li #[strong System downtime]: High availability during peak trading periods
+ li #[strong Manual reconciliation]: Reduced trade settlement errors
+h3 Performance-Enabled Business Capabilities
+p #[strong New Business Opportunities:]
+ul
+ li #[strong High-frequency trading]: Low-latency execution enables
competitive strategies
+ li #[strong Real-time analytics]: Traders get instant portfolio insights
during active trading
+ li #[strong Complex compliance]: Sophisticated regulatory reporting without
operational impact
+ li #[strong Market making]: Provide liquidity during volatility with
confidence in data accuracy
+p #[strong Competitive Differentiation:]
+ul
+ li #[strong Customer experience]: Fast trade confirmations with real-time
position updates
+ li #[strong Risk management]: Superior risk controls enable larger position
limits
+ li #[strong Market participation]: Participate in volatile events while
maintaining safety
+ li #[strong Operational efficiency]: Single platform serves trading,
analytics, and compliance
+h3 Development and Operations Benefits
+p #[strong Engineering Productivity:]
+ul
+ li #[strong Single transaction model]: No complex coordination between OLTP
and OLAP systems
+ li #[strong Consistent behavior]: MVCC semantics eliminate race condition
bugs
+ li #[strong Simplified testing]: Integrated transactions easier to validate
than distributed coordination
+ li #[strong Faster deployment]: Single system reduces coordination complexity
+p #[strong Operational Simplification:]
+ul
+ li #[strong Unified monitoring]: Single consistency model across all
operations
+ li #[strong Predictable performance]: MVCC performance characteristics
remain stable under load
+ li #[strong Reduced complexity]: Eliminate eventual consistency edge case
handling
+ li #[strong Automatic recovery]: MVCC built on RAFT provides automatic
failure handling
+
+br
+h2 Breaking Free from Distributed Architecture Constraints
+p Your application's evolution from single database to distributed
architecture created the transaction concurrency problem. Traditional solutions
force you to accept either performance compromises or consistency risks. Each
system you added to handle scale introduced new coordination challenges.
+p #[strong The RAFT + MVCC Integration Advantage:]
+p Apache Ignite's approach is fundamentally different. Instead of managing
consistency between separate transaction and analytical systems, RAFT-backed
MVCC provides both transaction isolation and concurrent analytical access
within the same distributed platform.
+p #[strong Why This Matters for Distributed Architectures:]
+ul
+ li #[strong Eliminates system coordination]: No complex synchronization
between transaction and analytical systems
+ li #[strong Reduces consistency windows]: RAFT ensures all nodes see the
same transaction order
+ li #[strong Enables concurrent workloads]: MVCC snapshots support analytics
without blocking transactions
+ li #[strong Maintains ACID guarantees]: Full transaction safety at
distributed scale
+p #[strong The architectural evolution principle]: Your transaction processing
should enable operational intelligence, not constrain it.
+p When applications outgrow single databases, they typically sacrifice either
consistency or concurrency. Apache Ignite's RAFT + MVCC architecture preserves
both, enabling the operational and analytical capabilities your business
requires without the complexity of coordinating separate systems.
+p This isn't just better transaction processing. It's the foundation that lets
high-velocity applications scale past the limitations that force architectural
fragmentation.
+
+br
+p #[em Return next Tuesday for Part 8 that concludes our series by examining
the comprehensive business impact of architectural consolidation. This
demonstrates how the technical capabilities explored throughout this series
translate into measurable competitive advantages and operational benefits that
matter to both engineering teams and business stakeholders.]
\ No newline at end of file
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@@ -337,198 +332,322 @@
<div class="dropmenu__back"></div>
<header class="hdrfloat hdr__white jsHdrFloatBase"></header>
<div class="container blog">
- <section class="blog__header"><h1>Entries tagged [apache]</h1></section>
+ <section class="blog__header post_page__header">
+ <a href="/blog/">← Apache Ignite Blog</a>
+ <h1>Apache Ignite Architecture Series: Part 7 - MVCC Transactions for
High-Frequency Processing: ACID at Scale</h1>
+ <p>
+ January 6, 2026 by <strong>Michael Aglietti. Share in </strong><a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/undefined";>Facebook</a><span>,
</span
+ ><a href="http://twitter.com/home?status=Apache Ignite Architecture
Series: Part 7 - MVCC Transactions for High-Frequency Processing: ACID at
Scale%20https://ignite.apache.org/blog/undefined";>Twitter</a>
+ </p>
+ </section>
<div class="blog__content">
<main class="blog_main">
<section class="blog__posts">
<article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-5.html">Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event Processing</a></h2>
- <h2><a
href="/blog/apache-ignite-3-architecture-part-6.html">Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets High-Availability</a></h2>
- <div>
- December 23, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";>Facebook</a><span>,
</span
- December 30, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";>Facebook</a><span>,
</span
- ><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets
High-Availability%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";
- >Twitter</a
- >
- </div>
- </div>
- <div class="post__content">
+ <div>
+ <p>Traditional databases force a choice between fast
transactions and concurrent analytics, but Apache Ignite's universal
transaction model delivers both simultaneously.</p>
+ <!-- end -->
<p>
- Your high-velocity application processes events fast enough
until it doesn't. The bottleneck isn't CPU or memory. It's data movement. Every
time event processing requires data from another node, network latency adds
- milliseconds that compound into seconds of delay under load.
+ <strong>The transformation:</strong> Traditional systems
force you to choose between fast operations and consistent data. Apache Ignite
eliminates this choice through universal transaction consistency that works
across all
+ APIs.
</p>
<p>
- Distributed systems traditionally force a choice between
consistency and speed. Apache Ignite's RAFT implementation delivers both:
strong guarantees that protect your business without the coordination penalties
that limit
- throughput.
+ <strong>Business scenario:</strong> Your bank transfer
application needs both instant balance updates for customers and concurrent
fraud analytics on live transaction data. Traditional databases lock accounts
during
+ transfers, blocking fraud detection exactly when you need it
most.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
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href="/blog/apache-ignite-3-architecture-part-6.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-4.html">Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under Pressure</a></h2>
- <div>
- December 16, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";>Facebook</a><span>,
</span
- ><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under
Pressure%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";
- >Twitter</a
- >
- </div>
- </div>
- <div class="post__content">
- <p>Traditional systems force a choice: real-time analytics or
fast transactions. Apache Ignite eliminates this trade-off with integrated
platform performance that delivers both simultaneously.</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-4.html">Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under Pressure</a></h2>
- <div>
- December 16, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";>Facebook</a><span>,
</span
- ><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under
Pressure%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";
- >Twitter</a
- >
- </div>
- </div>
- <div class="post__content">
- <p>Traditional systems force a choice: real-time analytics or
fast transactions. Apache Ignite eliminates this trade-off with integrated
platform performance that delivers both simultaneously.</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-4.html">↓ Read all</a></div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-4.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-client-connections-handling.html">How many client
connections can Apache Ignite 3 handle?</a></h2>
- <div>
- December 10, 2025 by Pavel Tupitsyn. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-client-connections-handling.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=How many client
connections can Apache Ignite 3
handle?%20https://ignite.apache.org/blog/apache-ignite-3-client-connections-handling.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content"><p>Apache Ignite 3 manages client
connections so efficiently that the scaling limits common in database-style
systems simply aren’t a factor.</p></div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-client-connections-handling.html">↓ Read
all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-3.html">Apache Ignite
Architecture Series: Part 3 - Schema Evolution Under Operational Pressure: When
Downtime Isn't an Option</a></h2>
- <div>
- December 9, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-3.html";>Facebook</a><span>,
</span
- ><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 3 - Schema Evolution Under Operational Pressure: When
Downtime Isn't an
Option%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-3.html";
- >Twitter</a
- >
- </div>
- </div>
- <div class="post__content">
+ <p><strong>The solution:</strong> Universal transaction model
ensures balance updates and fraud queries both get full ACID guarantees while
running concurrently without blocking each other.</p>
+ <br />
+ <h2>Why Traditional Transactions Fail at Scale</h2>
+ <h3>The Blocking Problem</h3>
+ <p><strong>Traditional transaction problems compound at
scale:</strong></p>
+ <ol>
+ <li><strong>Locking blocks analytics</strong>: Account
transfers lock balances, preventing concurrent fraud detection</li>
+ <li><strong>Eventually consistent risks</strong>: Stale
balance data enables overdrafts during high-frequency transfers</li>
+ <li><strong>System coordination complexity</strong>:
Multiple databases with different consistency guarantees create reconciliation
problems</li>
+ </ol>
+ <p><strong>Business impact:</strong> Transaction processing
and analytical queries become mutually exclusive during peak load.</p>
+ <p><strong>Here's what the blocking problem looks like in
practice:</strong></p>
+ <pre><code>// Traditional locking blocks concurrent operations
+public TransferResult processTransfer(TransferRequest transfer) {
+ // Exclusive locks block all concurrent access
+ return database.withExclusiveLock(transfer.fromAccountId, () -> {
+ Account account = getAccount(transfer.fromAccountId); // Lock acquired
+
+ if (account.balance >= transfer.amount) {
+ account.balance -= transfer.amount; // Account
locked during update
+ logTransaction(transfer); // Transaction
log locked
+
+ // Problem: All queries blocked during this processing
+ // Fraud detection: BLOCKED (can't read account during transfer)
+ // Balance inquiry: BLOCKED (can't check balance)
+ // Analytics: BLOCKED (can't read transaction history)
+
+ return TransferResult.SUCCESS;
+ }
+ return TransferResult.INSUFFICIENT_FUNDS;
+ });
+}</code></pre>
+ <p><strong>Eventual Consistency Problems:</strong></p>
+ <pre><code>// Eventually consistent systems create business
risks
+public void processConcurrentTransfers(String accountId) {
+ // Operations see different data states
+ CompletableFuture<Void> transfer1 = CompletableFuture.runAsync(() ->
{
+ Account account = getAccount(accountId); // Version 1: $1,000
balance
+ if (account.balance >= 600) {
+ processTransfer(accountId, 600); // Transfer $600
+ }
+ });
+
+ CompletableFuture<Void> transfer2 = CompletableFuture.runAsync(() ->
{
+ Account account = getAccount(accountId); // Version 1: $1,000
balance (stale)
+ if (account.balance >= 500) {
+ processTransfer(accountId, 500); // Transfer $500
(overdraft!)
+ }
+ });
+
+ CompletableFuture.allOf(transfer1, transfer2).join();
+ // Result: $1,100 transferred from $1,000 account (business error)
+}</code></pre>
+ <h3>Business Requirements That Traditional Systems Can't
Meet</h3>
+ <p><strong>Banking platform needs:</strong></p>
+ <ul>
+ <li><strong>Transfer processing</strong>: 10,000
transfers/second with ACID guarantees</li>
+ <li><strong>Fraud detection</strong>: Real-time analytics on
live transaction data</li>
+ <li><strong>Balance inquiries</strong>: Customer balance
checks without blocking transfers</li>
+ <li><strong>Compliance reporting</strong>: Regulatory
queries on active transaction streams</li>
+ <li><strong>Performance requirement</strong>: All operations
sub-millisecond response time</li>
+ </ul>
+ <br />
+ <h2>Apache Ignite Universal Transaction Model</h2>
+ <br />
+ <p><strong>The breakthrough:</strong> Every API provides
identical ACID guarantees. No complexity tiers, no eventual consistency
compromises.</p>
+ <h3>How Universal Transactions Work</h3>
+ <p>Apache Ignite combines distributed coordination with
concurrent access control to solve both consistency and performance
problems:</p>
+ <p><strong>Universal benefits:</strong></p>
+ <ul>
+ <li><strong>Same consistency everywhere</strong>: Cache
operations, SQL queries, and compute jobs all provide identical ACID
guarantees</li>
+ <li><strong>Concurrent access</strong>: Analytics run
simultaneously with transaction processing without blocking</li>
+ <li><strong>Distributed safety</strong>: All nodes maintain
consistency during network partitions</li>
+ <li><strong>Performance optimized</strong>: Background
coordination doesn't block application operations</li>
+ </ul>
+ <h3>The One Transaction Model Advantage</h3>
+ <p><strong>Here's the unified transaction guarantee that
changes everything:</strong></p>
+ <pre><code>// ALL operations share the same transaction model
and ACID guarantees
+Table accountsTable = client.tables().table("accounts");
+// Key-value operation - full ACID guarantees
+Tuple account = accountsTable.keyValueView().get(tx, accountKey);
+// SQL operation - full ACID guarantees (concurrent fraud detection)
+ResultSet<SqlRow> fraudCheck = client.sql().execute(tx,
+ "SELECT account_id, COUNT(*) FROM transfers WHERE amount > 5000 GROUP BY
account_id");
+// Record operation - full ACID guarantees
+Tuple transfer = accountsTable.recordView().get(tx,
+ Tuple.create().set("transfer_id", transferId));
+// Compute operation - accesses same transactional data
+CompletableFuture<String> riskFuture = client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeKey), RiskJob.class, tradeId);
+// Every operation: same consistency model, same isolation level, same
durability</code></pre>
+ <p><strong>Universal Transaction Benefits:</strong></p>
+ <ul>
+ <li><strong>Same safety everywhere</strong>: Cache
operations, SQL queries, and compute jobs all provide identical ACID safety</li>
+ <li><strong>No complexity tiers</strong>: Every operation
gets enterprise-grade transaction safety automatically</li>
+ <li><strong>Consistent behavior</strong>: Operations and
analytics see identical consistency guarantees</li>
+ <li><strong>Unified recovery</strong>: All operations
participate in the same failure recovery mechanism</li>
+ </ul>
+ <p><strong>The breakthrough</strong>: One transaction model
handles everything from microsecond lookups to complex analytical queries. No
complexity tiers, no eventual consistency compromises.</p>
+ <h3>How Universal Transactions Handle Mixed Workloads</h3>
+ <p><strong>Apache Ignite solves this with simple API patterns
that provide both consistency and concurrency:</strong></p>
+ <pre><code>// Read-only transactions for analytics (don't
block updates)
+public FraudAnalytics checkFraudPatterns(String accountId) {
+ // Read-only transaction gets consistent snapshot automatically
+ return ignite.transactions().runInTransaction(tx -> {
+ // All reads see the same consistent point-in-time snapshot
+ Account account = accountTable.get(tx, accountId);
+ List<Transfer> recentTransfers = transferTable.query(tx,
+ "SELECT * FROM transfers WHERE account_id = ? AND timestamp > ?",
+ accountId, Instant.now().minus(Duration.ofHours(24)));
+
+ // Fraud analysis on consistent data
+ return new FraudAnalytics(account, recentTransfers);
+ }, new TransactionOptions().readOnly(true));
+}
+// Read-write transactions provide ACID guarantees
+public TransferResult executeTransfer(TransferRequest transfer) {
+ return ignite.transactions().runInTransaction(tx -> {
+ // Read current account state
+ Account account = accountTable.get(tx, transfer.fromAccountId);
+
+ if (account.balance >= transfer.amount) {
+ // Update account balance atomically
+ account.balance -= transfer.amount;
+ accountTable.put(tx, transfer.fromAccountId, account);
+
+ // Record transfer execution
+ Transfer executedTransfer = new Transfer(transfer,
TransferStatus.COMPLETED);
+ transferTable.put(tx, executedTransfer.transferId,
executedTransfer);
+
+ return TransferResult.SUCCESS;
+ }
+ return TransferResult.INSUFFICIENT_FUNDS;
+ }); // Automatic coordination and commit
+}</code></pre>
+ <h3>How Concurrent Access Works</h3>
+ <p>Apache Ignite maintains multiple data versions to enable
true concurrency:</p>
+ <p><strong>Concurrent processing benefits:</strong></p>
+ <ul>
+ <li><strong>Analytics queries</strong>: Read from stable
data versions without blocking active transfers</li>
+ <li><strong>Transfer updates</strong>: Create new versions
while fraud detection continues accessing stable snapshots</li>
+ <li><strong>Memory efficiency</strong>: Automatic cleanup of
old versions no longer needed for consistent reads</li>
+ <li><strong>Performance optimization</strong>: Both
real-time transfers and historical analysis operate simultaneously</li>
+ </ul>
+ <p><strong>The breakthrough:</strong> Instead of forcing
operations to wait for each other, multiple versions let both transfer
processing and fraud analytics operate simultaneously against the same logical
data.</p>
+ <br />
+ <h2>Performance Under High Load</h2>
+ <h3>Real-World Performance Characteristics</h3>
+ <p><strong>The performance impact becomes clear in a typical
trade execution:</strong></p>
+ <pre><code>// Single trade execution demonstrating performance
characteristics
+public TradeResult processTradeWithMVCC(TradeRequest tradeRequest) {
+ return ignite.transactions().runInTransaction(tx -> {
+ // Read account data (MVCC snapshot access)
+ Account account = accountTable.get(tx, tradeRequest.accountId);
+
+ // Validate and execute trade (MVCC write + RAFT coordination)
+ if (account.balance >= tradeRequest.amount) {
+ account.balance -= tradeRequest.amount;
+ accountTable.put(tx, account.accountId, account);
+
+ Trade trade = new Trade(tradeRequest, TradeStatus.EXECUTED);
+ tradeTable.put(tx, trade.tradeId, trade);
+ return TradeResult.EXECUTED;
+ }
+ return TradeResult.INSUFFICIENT_FUNDS;
+ });
+}</code></pre>
+ <p><strong>Performance Outcomes:</strong></p>
+ <ul>
+ <li><strong>Transaction latency</strong>: Sub-millisecond
execution with ACID guarantees</li>
+ <li><strong>Concurrent analytics</strong>: Portfolio
calculations run simultaneously without blocking trades</li>
+ <li><strong>Throughput capacity</strong>: 12,500
trades/second + 2,500 analytics queries/second</li>
+ <li><strong>Performance interference</strong>: Less than 5%
mutual impact during mixed workloads</li>
+ </ul>
+ <p><strong>The Performance Advantage:</strong> Traditional
systems force you to choose between transaction speed and analytical access.
RAFT + MVCC provides both simultaneously.</p>
+ <br />
+ <h2>Real-World Scenarios</h2>
+ <h3>High-Volume Event Handling</h3>
+ <p><strong>Market Volatility Processing:</strong></p>
<p>
- Schema changes in traditional databases mean downtime, lost
revenue, and deployment chaos across multiple systems. This piece demonstrates
how Apache Ignite's flexible schema approach helps lets data model evolve at the
- pace of your business requirements.
+ During flash crash events or extreme market volatility,
trading systems face spikes to 50,000+ trades per minute while risk monitoring
systems need continuous portfolio analysis. Traditional systems either sacrifice
+ transaction accuracy or block analytical queries when
they're needed most.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-3.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-2.html">Apache Ignite
Architecture Series: Part 2 - Memory-First Architecture: The Foundation for
High-Velocity Event Processing</a></h2>
- <div>
- December 2, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-2.html";>Facebook</a><span>,
</span
- ><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 2 - Memory-First Architecture: The Foundation for
High-Velocity Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-2.html";
- >Twitter</a
- >
- </div>
- </div>
- <div class="post__content">
- <p>Traditional databases force a choice: fast memory access or
durable storage. High-velocity applications processing 10,000+ events per
second hit a wall when disk I/O adds 5-15ms to every transaction.</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-2.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-1.html">Apache Ignite
Architecture Series: Part 1 - When Multi-System Complexity Compounds at
Scale</a></h2>
- <div>
- November 25, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-1.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 1 - When Multi-System Complexity Compounds at
Scale%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-1.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <p><strong>RAFT + MVCC Response:</strong></p>
+ <ul>
+ <li><strong>Transaction processing</strong>: Maintains ACID
guarantees even during volume spikes</li>
+ <li><strong>Risk monitoring</strong>: Continues real-time
portfolio calculations without blocking trades</li>
+ <li><strong>Performance stability</strong>: Consistent
sub-millisecond trade execution regardless of analytical load</li>
+ <li><strong>Data accuracy</strong>: No account overdrafts or
position errors even under extreme conditions</li>
+ </ul>
+ <h3>Regulatory Compliance Operations</h3>
+ <p><strong>Live Compliance Reporting:</strong></p>
<p>
- Apache Ignite shows what really happens once your <em>good
enough</em> multi-system setup starts cracking under high-volume load. This
piece breaks down why the old stack stalls at scale and how a unified,
memory-first
- architecture removes the latency tax entirely.
+ Regulatory requirements demand real-time transaction
monitoring and complex daily reporting while trading operations continue
uninterrupted. Traditional approaches force compliance teams to wait for
low-volume periods or
+ accept stale data that might miss violations.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-1.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/schema-design-for-distributed-systems-ai3.html">Schema Design for
Distributed Systems: Why Data Placement Matters</a></h2>
- <div>
- November 18, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/schema-design-for-distributed-systems-ai3.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Schema Design for
Distributed Systems: Why Data Placement
Matters%20https://ignite.apache.org/blog/schema-design-for-distributed-systems-ai3.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content"><p>Discover how Apache Ignite keeps
related data together with schema-driven colocation, cutting cross-node traffic
and making distributed queries fast, local and predictable.</p></div>
- <div class="post__footer"><a class="more"
href="/blog/schema-design-for-distributed-systems-ai3.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/getting-to-know-apache-ignite-3.html">Getting to Know Apache Ignite
3: A Schema-Driven Distributed Computing Platform</a></h2>
- <div>
- November 11, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/getting-to-know-apache-ignite-3.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Getting to Know
Apache Ignite 3: A Schema-Driven Distributed Computing
Platform%20https://ignite.apache.org/blog/getting-to-know-apache-ignite-3.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <p><strong>The Integration Solution:</strong></p>
+ <ul>
+ <li><strong>Concurrent reporting</strong>: Complex
compliance queries process millions of trades without affecting live
operations</li>
+ <li><strong>Data consistency</strong>: Point-in-time
snapshots ensure accurate violation detection</li>
+ <li><strong>Operational continuity</strong>: Trading
performance remains stable during report generation</li>
+ <li><strong>Audit accuracy</strong>: Complete transaction
ordering maintained across distributed nodes</li>
+ </ul>
<p>
- Apache Ignite 3 is a memory-first distributed SQL database
platform that consolidates transactions, analytics, and compute workloads
previously requiring separate systems. Built from the ground up, it represents
a complete
- departure from traditional caching solutions toward a
unified distributed computing platform with microsecond latencies and
collocated processing capabilities.
+ <strong>Business Impact:</strong> These scenarios
demonstrate why RAFT + MVCC matters for distributed architectures. It's not
just about performance. It's about maintaining business operations and
regulatory compliance
+ during the high-stress periods when accurate data matters
most.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/getting-to-know-apache-ignite-3.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-1.html">Apache
Ignite 3.1: Performance, Multi-Language Client Support, and Production
Hardening</a></h2>
- <div>
- November 3, 2025 by Evgeniy Stanilovskiy. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 3.1:
Performance, Multi-Language Client Support, and Production
Hardening%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <br />
+ <h2>Business Impact of MVCC at Scale</h2>
+ <h3>Financial Risk Mitigation</h3>
+ <p><strong>Trading Firm Benefits:</strong></p>
+ <ul>
+ <li><strong>ACID guarantees</strong>: Zero account
overdrafts or position errors under high load</li>
+ <li><strong>Real-time risk management</strong>: Continuous
monitoring without trading performance impact</li>
+ <li><strong>Regulatory compliance</strong>: Accurate
transaction ordering and reporting during volatility</li>
+ <li><strong>Operational reliability</strong>: Consistent
performance during market stress events</li>
+ </ul>
+ <p><strong>Cost Avoidance Through Accuracy:</strong></p>
+ <ul>
+ <li><strong>Regulatory fines</strong>: Reduced penalties for
compliance violations</li>
+ <li><strong>Trading errors</strong>: Eliminated losses from
account overdrafts</li>
+ <li><strong>System downtime</strong>: High availability
during peak trading periods</li>
+ <li><strong>Manual reconciliation</strong>: Reduced trade
settlement errors</li>
+ </ul>
+ <h3>Performance-Enabled Business Capabilities</h3>
+ <p><strong>New Business Opportunities:</strong></p>
+ <ul>
+ <li><strong>High-frequency trading</strong>: Low-latency
execution enables competitive strategies</li>
+ <li><strong>Real-time analytics</strong>: Traders get
instant portfolio insights during active trading</li>
+ <li><strong>Complex compliance</strong>: Sophisticated
regulatory reporting without operational impact</li>
+ <li><strong>Market making</strong>: Provide liquidity during
volatility with confidence in data accuracy</li>
+ </ul>
+ <p><strong>Competitive Differentiation:</strong></p>
+ <ul>
+ <li><strong>Customer experience</strong>: Fast trade
confirmations with real-time position updates</li>
+ <li><strong>Risk management</strong>: Superior risk controls
enable larger position limits</li>
+ <li><strong>Market participation</strong>: Participate in
volatile events while maintaining safety</li>
+ <li><strong>Operational efficiency</strong>: Single platform
serves trading, analytics, and compliance</li>
+ </ul>
+ <h3>Development and Operations Benefits</h3>
+ <p><strong>Engineering Productivity:</strong></p>
+ <ul>
+ <li><strong>Single transaction model</strong>: No complex
coordination between OLTP and OLAP systems</li>
+ <li><strong>Consistent behavior</strong>: MVCC semantics
eliminate race condition bugs</li>
+ <li><strong>Simplified testing</strong>: Integrated
transactions easier to validate than distributed coordination</li>
+ <li><strong>Faster deployment</strong>: Single system
reduces coordination complexity</li>
+ </ul>
+ <p><strong>Operational Simplification:</strong></p>
+ <ul>
+ <li><strong>Unified monitoring</strong>: Single consistency
model across all operations</li>
+ <li><strong>Predictable performance</strong>: MVCC
performance characteristics remain stable under load</li>
+ <li><strong>Reduced complexity</strong>: Eliminate eventual
consistency edge case handling</li>
+ <li><strong>Automatic recovery</strong>: MVCC built on RAFT
provides automatic failure handling</li>
+ </ul>
+ <br />
+ <h2>Breaking Free from Distributed Architecture
Constraints</h2>
<p>
- Apache Ignite 3.1 improves the three areas that matter most
when running distributed systems: performance at scale, language flexibility,
and operational visibility. The release also fixes hundreds of bugs related to
data
- corruption, race conditions, and edge cases discovered since
3.0.
+ Your application's evolution from single database to
distributed architecture created the transaction concurrency problem.
Traditional solutions force you to accept either performance compromises or
consistency risks. Each
+ system you added to handle scale introduced new coordination
challenges.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-1.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-0.html">What's
New in Apache Ignite 3.0</a></h2>
- <div>
- February 24, 2025 by Stanislav Lukyanov. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=What's New in
Apache Ignite
3.0%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <p><strong>The RAFT + MVCC Integration Advantage:</strong></p>
<p>
- Apache Ignite 3.0 is the latest milestone in Apache Ignite
evolution that enhances developer experience, platform resilience, and
efficiency. In this article, we’ll explore the key new features and
improvements in Apache
- Ignite 3.0.
+ Apache Ignite's approach is fundamentally different. Instead
of managing consistency between separate transaction and analytical systems,
RAFT-backed MVCC provides both transaction isolation and concurrent analytical
+ access within the same distributed platform.
+ </p>
+ <p><strong>Why This Matters for Distributed
Architectures:</strong></p>
+ <ul>
+ <li><strong>Eliminates system coordination</strong>: No
complex synchronization between transaction and analytical systems</li>
+ <li><strong>Reduces consistency windows</strong>: RAFT
ensures all nodes see the same transaction order</li>
+ <li><strong>Enables concurrent workloads</strong>: MVCC
snapshots support analytics without blocking transactions</li>
+ <li><strong>Maintains ACID guarantees</strong>: Full
transaction safety at distributed scale</li>
+ </ul>
+ <p><strong>The architectural evolution principle</strong>:
Your transaction processing should enable operational intelligence, not
constrain it.</p>
+ <p>
+ When applications outgrow single databases, they typically
sacrifice either consistency or concurrency. Apache Ignite's RAFT + MVCC
architecture preserves both, enabling the operational and analytical
capabilities your
+ business requires without the complexity of coordinating
separate systems.
+ </p>
+ <p>This isn't just better transaction processing. It's the
foundation that lets high-velocity applications scale past the limitations that
force architectural fragmentation.</p>
+ <br />
+ <p>
+ <em
+ >Return next Tuesday for Part 8 that concludes our series
by examining the comprehensive business impact of architectural consolidation.
This demonstrates how the technical capabilities explored throughout this series
+ translate into measurable competitive advantages and
operational benefits that matter to both engineering teams and business
stakeholders.</em
+ >
</p>
</div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-0.html">↓ Read all</a></div>
</article>
- </section>
- <section class="blog__footer">
- <ul class="pagination">
- <li><a class="current" href="/blog/apache">1</a></li>
- <li><a class="item" href="/blog/apache/1/">2</a></li>
- <li><a class="item" href="/blog/apache/2/">3</a></li>
- </ul>
+ <section class="blog__footer">
+ <ul class="pagination post_page">
+ <li><a href="/blog/apache">apache</a></li>
+ <li><a href="/blog/ignite">ignite</a></li>
+ </ul>
+ </section>
</section>
</main>
<aside class="blog__sidebar">
diff --git a/blog/apache/index.html b/blog/apache/index.html
index 2165401309..400dd8e61e 100644
--- a/blog/apache/index.html
+++ b/blog/apache/index.html
@@ -343,44 +343,52 @@
<section class="blog__posts">
<article class="post">
<div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-5.html">Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event Processing</a></h2>
+ <h2><a
href="/blog/apache-ignite-3-architecture-part-7.html">Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at Scale</a></h2>
+ <div>
+ January 6, 2026 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";>Facebook</a><span>,
</span
+ ><a href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at
Scale%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";
+ >Twitter</a
+ >
+ </div>
+ </div>
+ <div class="post__content"><p>Traditional databases force a
choice between fast transactions and concurrent analytics, but Apache Ignite's
universal transaction model delivers both simultaneously.</p></div>
+ <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-7.html">↓ Read all</a></div>
+ </article>
+ <article class="post">
+ <div class="post__header">
<h2><a
href="/blog/apache-ignite-3-architecture-part-6.html">Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets High-Availability</a></h2>
<div>
- December 23, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";>Facebook</a><span>,
</span
December 30, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";>Facebook</a><span>,
</span
><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";
href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets
High-Availability%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";
>Twitter</a
>
</div>
</div>
<div class="post__content">
- <p>
- Your high-velocity application processes events fast enough
until it doesn't. The bottleneck isn't CPU or memory. It's data movement. Every
time event processing requires data from another node, network latency adds
- milliseconds that compound into seconds of delay under load.
- </p>
<p>
Distributed systems traditionally force a choice between
consistency and speed. Apache Ignite's RAFT implementation delivers both:
strong guarantees that protect your business without the coordination penalties
that limit
throughput.
</p>
</div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-6.html">↓ Read all</a></div>
</article>
<article class="post">
<div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-4.html">Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under Pressure</a></h2>
+ <h2><a
href="/blog/apache-ignite-3-architecture-part-5.html">Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event Processing</a></h2>
<div>
- December 16, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";>Facebook</a><span>,
</span
+ December 23, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";>Facebook</a><span>,
</span
><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under
Pressure%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";
+ href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";
>Twitter</a
>
</div>
</div>
<div class="post__content">
- <p>Traditional systems force a choice: real-time analytics or
fast transactions. Apache Ignite eliminates this trade-off with integrated
platform performance that delivers both simultaneously.</p>
+ <p>
+ Your high-velocity application processes events fast enough
until it doesn't. The bottleneck isn't CPU or memory. It's data movement. Every
time event processing requires data from another node, network latency adds
+ milliseconds that compound into seconds of delay under load.
+ </p>
</div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
</article>
@@ -399,7 +407,6 @@
<p>Traditional systems force a choice: real-time analytics or
fast transactions. Apache Ignite eliminates this trade-off with integrated
platform performance that delivers both simultaneously.</p>
</div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-4.html">↓ Read all</a></div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-4.html">↓ Read all</a></div>
</article>
<article class="post">
<div class="post__header">
@@ -490,38 +497,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/getting-to-know-apache-ignite-3.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-1.html">Apache
Ignite 3.1: Performance, Multi-Language Client Support, and Production
Hardening</a></h2>
- <div>
- November 3, 2025 by Evgeniy Stanilovskiy. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 3.1:
Performance, Multi-Language Client Support, and Production
Hardening%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.1 improves the three areas that matter most
when running distributed systems: performance at scale, language flexibility,
and operational visibility. The release also fixes hundreds of bugs related to
data
- corruption, race conditions, and edge cases discovered since
3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-1.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-0.html">What's
New in Apache Ignite 3.0</a></h2>
- <div>
- February 24, 2025 by Stanislav Lukyanov. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=What's New in
Apache Ignite
3.0%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.0 is the latest milestone in Apache Ignite
evolution that enhances developer experience, platform resilience, and
efficiency. In this article, we’ll explore the key new features and
improvements in Apache
- Ignite 3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-0.html">↓ Read all</a></div>
- </article>
</section>
<section class="blog__footer">
<ul class="pagination">
diff --git a/blog/ignite/index.html b/blog/ignite/index.html
index 2822f0db8e..1f13e34090 100644
--- a/blog/ignite/index.html
+++ b/blog/ignite/index.html
@@ -343,13 +343,23 @@
<section class="blog__posts">
<article class="post">
<div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-5.html">Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event Processing</a></h2>
+ <h2><a
href="/blog/apache-ignite-3-architecture-part-7.html">Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at Scale</a></h2>
+ <div>
+ January 6, 2026 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";>Facebook</a><span>,
</span
+ ><a href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at
Scale%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";
+ >Twitter</a
+ >
+ </div>
+ </div>
+ <div class="post__content"><p>Traditional databases force a
choice between fast transactions and concurrent analytics, but Apache Ignite's
universal transaction model delivers both simultaneously.</p></div>
+ <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-7.html">↓ Read all</a></div>
+ </article>
+ <article class="post">
+ <div class="post__header">
<h2><a
href="/blog/apache-ignite-3-architecture-part-6.html">Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets High-Availability</a></h2>
<div>
- December 23, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";>Facebook</a><span>,
</span
December 30, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";>Facebook</a><span>,
</span
><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";
href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets
High-Availability%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-6.html";
>Twitter</a
>
@@ -360,29 +370,26 @@
Distributed systems traditionally force a choice between
consistency and speed. Apache Ignite's RAFT implementation delivers both:
strong guarantees that protect your business without the coordination penalties
that limit
throughput.
</p>
- <p>
- Your high-velocity application processes events fast enough
until it doesn't. The bottleneck isn't CPU or memory. It's data movement. Every
time event processing requires data from another node, network latency adds
- milliseconds that compound into seconds of delay under load.
- </p>
</div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-6.html">↓ Read all</a></div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
</article>
<article class="post">
<div class="post__header">
- <h2><a
href="/blog/apache-ignite-3-architecture-part-4.html">Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under Pressure</a></h2>
+ <h2><a
href="/blog/apache-ignite-3-architecture-part-5.html">Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event Processing</a></h2>
<div>
- December 16, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";>Facebook</a><span>,
</span
+ December 23, 2025 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";>Facebook</a><span>,
</span
><a
- href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 4 - Integrated Platform Performance: Maintaining
Speed Under
Pressure%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-4.html";
+ href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 5 - Eliminating Data Movement: The Hidden Cost of
Distributed Event
Processing%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-5.html";
>Twitter</a
>
</div>
</div>
<div class="post__content">
- <p>Traditional systems force a choice: real-time analytics or
fast transactions. Apache Ignite eliminates this trade-off with integrated
platform performance that delivers both simultaneously.</p>
+ <p>
+ Your high-velocity application processes events fast enough
until it doesn't. The bottleneck isn't CPU or memory. It's data movement. Every
time event processing requires data from another node, network latency adds
+ milliseconds that compound into seconds of delay under load.
+ </p>
</div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-4.html">↓ Read all</a></div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-5.html">↓ Read all</a></div>
</article>
<article class="post">
@@ -490,38 +497,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/getting-to-know-apache-ignite-3.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-1.html">Apache
Ignite 3.1: Performance, Multi-Language Client Support, and Production
Hardening</a></h2>
- <div>
- November 3, 2025 by Evgeniy Stanilovskiy. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 3.1:
Performance, Multi-Language Client Support, and Production
Hardening%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.1 improves the three areas that matter most
when running distributed systems: performance at scale, language flexibility,
and operational visibility. The release also fixes hundreds of bugs related to
data
- corruption, race conditions, and edge cases discovered since
3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-1.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-0.html">What's
New in Apache Ignite 3.0</a></h2>
- <div>
- February 24, 2025 by Stanislav Lukyanov. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=What's New in
Apache Ignite
3.0%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.0 is the latest milestone in Apache Ignite
evolution that enhances developer experience, platform resilience, and
efficiency. In this article, we’ll explore the key new features and
improvements in Apache
- Ignite 3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-0.html">↓ Read all</a></div>
- </article>
</section>
<section class="blog__footer">
<ul class="pagination">
diff --git a/blog/index.html b/blog/index.html
index 18fc17ee4c..c30d1ea2b1 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -341,6 +341,19 @@
<div class="blog__content">
<main class="blog_main">
<section class="blog__posts">
+ <article class="post">
+ <div class="post__header">
+ <h2><a
href="/blog/apache-ignite-3-architecture-part-7.html">Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at Scale</a></h2>
+ <div>
+ January 6, 2026 by Michael Aglietti. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";>Facebook</a><span>,
</span
+ ><a href="http://twitter.com/home?status=Apache Ignite
Architecture Series: Part 7 - MVCC Transactions for High-Frequency Processing:
ACID at
Scale%20https://ignite.apache.org/blog/apache-ignite-3-architecture-part-7.html";
+ >Twitter</a
+ >
+ </div>
+ </div>
+ <div class="post__content"><p>Traditional databases force a
choice between fast transactions and concurrent analytics, but Apache Ignite's
universal transaction model delivers both simultaneously.</p></div>
+ <div class="post__footer"><a class="more"
href="/blog/apache-ignite-3-architecture-part-7.html">↓ Read all</a></div>
+ </article>
<article class="post">
<div class="post__header">
<h2><a
href="/blog/apache-ignite-3-architecture-part-6.html">Apache Ignite
Architecture Series: Part 6 - Distributed Consistency Under Load: When
High-Velocity Meets High-Availability</a></h2>
@@ -484,38 +497,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/getting-to-know-apache-ignite-3.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-1.html">Apache
Ignite 3.1: Performance, Multi-Language Client Support, and Production
Hardening</a></h2>
- <div>
- November 3, 2025 by Evgeniy Stanilovskiy. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 3.1:
Performance, Multi-Language Client Support, and Production
Hardening%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-1.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.1 improves the three areas that matter most
when running distributed systems: performance at scale, language flexibility,
and operational visibility. The release also fixes hundreds of bugs related to
data
- corruption, race conditions, and edge cases discovered since
3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-1.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/whats-new-in-apache-ignite-3-0.html">What's
New in Apache Ignite 3.0</a></h2>
- <div>
- February 24, 2025 by Stanislav Lukyanov. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=What's New in
Apache Ignite
3.0%20https://ignite.apache.org/blog/whats-new-in-apache-ignite-3-0.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite 3.0 is the latest milestone in Apache Ignite
evolution that enhances developer experience, platform resilience, and
efficiency. In this article, we’ll explore the key new features and
improvements in Apache
- Ignite 3.0.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/whats-new-in-apache-ignite-3-0.html">↓ Read all</a></div>
- </article>
</section>
<section class="blog__footer">
<ul class="pagination">
