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new 09361e9baf IGNITE-27209 Publish p3 of AI3 Architecture series (#289)
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commit 09361e9baff57f0b4b9a0ff227dc7f4aca9dcb48
Author: jinxxxoid <[email protected]>
AuthorDate: Tue Dec 9 20:59:27 2025 +0400
IGNITE-27209 Publish p3 of AI3 Architecture series (#289)
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+---
+title: "Apache Ignite Architecture Series: Part 3 - Schema Evolution Under
Operational Pressure: When Downtime Isn't an Option"
+author: "Michael Aglietti"
+date: 2025-12-09
+tags:
+ - apache
+ - ignite
+---
+
+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.
+
+<!-- end -->
+
+p Your high-velocity application runs 24/7. Customer expectations don't pause
for maintenance windows. Functional requirements evolve continuously. Yet
traditional database schema changes require downtime, coordination across
teams, and careful rollback planning.
+p Consider a payment processing system handling peak loads of 50,000
transactions per second. A new compliance rule requires additional
fraud-detection fields. Traditional schema changes would require:
+
+ul
+ li #[strong Coordinate downtime] across payment processing, fraud detection,
and reporting systems
+ li #[strong Apply schema changes] to primary database, read replicas, and
cache layers
+ li #[strong Deploy application updates] that work with new schema
+ li #[strong Validate data consistency] across all systems
+
+p #[strong Total downtime]: 2-4 hours. #[strong Lost revenue]: $500K+ for a
payment processor.
+
+p Apache Ignite 3 eliminates this constraint through flexible schema
evolution. Schema changes apply without downtime, applications adjust
automatically, and system operations continue uninterrupted.
+
+p #[strong The result: operational evolution without operational interruption.]
+
+hr
+br
+
+h2 The Schema Rigidity Problem at Scale
+
+h3 Traditional Schema Change Overhead
+
+p High-velocity applications face schema evolution challenges that compound
with scale:
+
+p #[strong Multi-System Schema Coordination:]
+
+pre.mermaid.
+ flowchart TB
+ subgraph "Schema Change Impact"
+ Downtime[Planned Downtime<br/>2-4 hours]
+ Revenue[Lost Revenue<br/>$500K+ per hour]
+ Risk[Deployment Risk<br/>Rollback complexity]
+ end
+
+ subgraph "Systems Requiring Updates"
+ Primary[(Primary Database<br/>Schema + Data Migration)]
+ Replica[(Read Replicas<br/>Replication Catch-up)]
+ Cache[(Cache Layer<br/>Schema Invalidation)]
+ Analytics[(Analytics Store<br/>ETL Pipeline Updates)]
+ Apps[Applications<br/>Code + Config Changes]
+ end
+
+ Primary --> Replica
+ Replica --> Cache
+ Cache --> Analytics
+ Analytics --> Apps
+
+ Apps --> Downtime
+ Downtime --> Revenue
+ Revenue --> Risk
+
+p #[strong The Compound Effect:]
+ul
+ li Each system adds coordination complexity
+ li Rollback procedures multiply with system count
+ li Testing requires full integration validation
+ li Deployment windows must accommodate slowest system
+
+h3 Real-World Schema Evolution Pressure
+
+p #[strong E-commerce Platform Evolution] (peak traffic: 100,000 orders per
second):
+br
+p #[strong Month 1]: Basic order processing
+
+pre
+ code.
+ CREATE TABLE orders (
+ order_id BIGINT PRIMARY KEY,
+ customer_id BIGINT,
+ product_id BIGINT,
+ amount DECIMAL(10,2),
+ status VARCHAR(20)
+ );
+
+br
+p #[strong Month 6]: Add payment processing compliance
+
+pre
+ code.
+ -- Traditional approach: downtime required
+ ALTER TABLE orders ADD COLUMN payment_method VARCHAR(50);
+ ALTER TABLE orders ADD COLUMN payment_processor VARCHAR(30);
+ ALTER TABLE orders ADD COLUMN compliance_data VARCHAR(500);
+ -- Requires: 2-hour maintenance window
+ -- Impact: $200K lost sales during downtime
+
+br
+p #[strong Month 12]: International expansion requirements
+
+pre
+ code.
+ -- More complexity: multiple system coordination
+ ALTER TABLE orders ADD COLUMN currency_code CHAR(3);
+ ALTER TABLE orders ADD COLUMN exchange_rate DECIMAL(10,6);
+ ALTER TABLE orders ADD COLUMN tax_jurisdiction VARCHAR(50);
+ ALTER TABLE orders ADD COLUMN shipping_region VARCHAR(50);
+ -- Requires: 4-hour coordinated deployment
+ -- Impact: $800K lost sales, customer complaints
+
+br
+p #[strong The Pattern]: Each functional change requires operational
disruption that grows with system complexity.
+
+hr
+br
+
+h2 Apache Ignite Flexible Schema Architecture
+
+h3 Catalog-Driven Schema Management
+
+p Apache Ignite manages schema evolution through versioned metadata catalogs
that coordinate changes across distributed nodes using hybrid logical clocks
for timestamp ordering:
+
+p #[strong Schema Evolution Process:]
+
+ul
+ li #[strong Validation Phase]: New columns validated against existing schema
+ li #[strong Atomic Update]: Schema change applied as single
`NewColumnsEntry` operation
+ li #[strong Version Management]: Applications operate against appropriate
schema version
+ li #[strong Cluster Coordination]: HybridTimestamp ensures consistent
activation
+
+p #[strong Key Advantages:]
+ul
+ li #[strong Atomic Updates]: Schema changes apply as single operations
+ li #[strong Version Management]: Applications can operate against different
schema versions
+ li #[strong Validation]: Automatic conflict detection and resolution
+ li #[strong Rollback]: Schema changes are reversible without data loss
+
+h3 Time-Based Schema Consistency
+
+p Schema changes coordinate across distributed nodes using cluster-wide
timestamps:
+
+p #[strong Consistency Guarantees:]
+ul
+ li #[strong Point-in-Time Activation]: All nodes apply schema changes
simultaneously
+ li #[strong Transaction Safety]: In-flight operations complete with original
schema
+ li #[strong Application Compatibility]: Gradual adoption without breaking
changes
+
+p The catalog management system uses `HybridTimestamp` values to ensure schema
versions activate consistently across all cluster nodes, preventing race
conditions and maintaining data integrity during schema evolution.
+
+hr
+br
+
+h2 Schema Evolution in Production
+
+h3 Adding Fraud Detection Fields (Real-Time)
+
+p #[strong Business Requirement]: New fraud detection requires additional
order data fields.
+
+p #[strong Traditional Approach]:
+
+pre
+ code.
+ -- Requires 2-hour maintenance window
+ BEGIN;
+ ALTER TABLE orders ADD COLUMN fraud_score DECIMAL(5,2);
+ ALTER TABLE orders ADD COLUMN risk_factors VARCHAR(500);
+ ALTER TABLE orders ADD COLUMN verification_status VARCHAR(30);
+ COMMIT;
+ -- Update application code (coordinated deployment)
+ -- Update cache schemas (cache invalidation)
+ -- Update analytics pipelines (ETL modifications)
+ -- Test end-to-end integration
+br
+p #[strong Apache Ignite Approach]:
+
+pre
+ code.
+ // Schema evolution during live operations
+ try (IgniteClient client =
IgniteClient.builder().addresses("cluster:10800").build()) {
+ // Add fraud detection columns without downtime
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ fraud_score DECIMAL(5,2) DEFAULT 0.0,
+ risk_factors VARCHAR(500) DEFAULT '',
+ verification_status VARCHAR(30) DEFAULT 'PENDING'
+ )
+ """);
+
+ // Applications immediately see new schema
+ // Existing queries continue working
+ // New functionality can use additional fields
+ }
+ // Application code adapts automatically
+ client.transactions().runInTransaction(tx -> {
+ // Existing order processing continues
+ client.sql().execute(tx, "INSERT INTO orders (order_id, customer_id,
amount) VALUES (?, ?, ?)",
+ orderId, customerId, amount);
+
+ // New fraud detection can use additional fields when ready
+ if (fraudDetectionEnabled) {
+ client.sql().execute(tx, "UPDATE orders SET fraud_score = ?,
risk_factors = ? WHERE order_id = ?",
+ fraudScore, riskFactors, orderId);
+ }
+ });
+
+p #[strong Result]:
+ul
+ li #[strong Downtime]: Zero
+ li #[strong Deployment coordination]: None required
+ li #[strong Revenue impact]: Zero
+ li #[strong Time to production]: Minutes instead of hours
+
+h3 Unified Schema Access Across APIs
+
+p #[strong Here's how the evolved schema works seamlessly across different
access patterns:]
+
+pre
+ code.
+ // The SAME evolved schema accessible through all APIs immediately
+ Table ordersTable = client.tables().table("orders");
+ // 1. Key-value access automatically sees new schema
+ Tuple orderTuple = ordersTable.keyValueView()
+ .get(tx, Tuple.create().set("order_id", orderId));
+ // New fields available: orderTuple.stringValue("verification_status")
+ // 2. SQL access uses new fraud detection fields immediately
+ ResultSet<SqlRow> suspiciousOrders = client.sql().execute(tx,
+ "SELECT order_id, fraud_score, risk_factors " +
+ "FROM orders WHERE fraud_score > 0.8 AND verification_status =
'REVIEW'");
+ // 3. Record access handles new fields through schema evolution
+ OrderRecord record = ordersTable.recordView()
+ .get(tx, new OrderRecord(orderId));
+ // OrderRecord.fraudScore now available without code changes
+
+p #[strong Schema Evolution Benefits:]
+ul
+ li #[strong No API fragmentation]: Same schema changes work across
key-value, SQL, and record APIs
+ li #[strong No deployment coordination]: All access patterns see schema
changes immediately
+ li #[strong No data migration]: New fields populate automatically with
defaults
+ li #[strong No downtime]: Live applications continue operating during schema
evolution
+
+p #[strong The unified schema advantage]: Schema changes apply once and work
immediately across all data access patterns, eliminating the multi-system
coordination that creates downtime.
+
+h3 International Expansion Schema Evolution
+
+p #[strong Business Requirement]: Support multiple currencies and tax
jurisdictions.
+
+pre
+ code.
+ // Progressive schema evolution for international expansion
+ public class InternationalExpansionEvolution {
+
+ public void addCurrencySupport(IgniteClient client) {
+ // Phase 1: Add currency fields (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ currency_code CHAR(3) DEFAULT 'USD',
+ exchange_rate DECIMAL(10,6) DEFAULT 1.0,
+ base_amount DECIMAL(10,2)
+ )
+ """);
+
+ // Applications continue working with existing USD logic
+ // New international orders can specify currency
+ }
+
+ public void addTaxSupport(IgniteClient client) {
+ // Phase 2: Add tax jurisdiction fields (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ tax_jurisdiction VARCHAR(50) DEFAULT 'US-FEDERAL',
+ tax_rate DECIMAL(5,4) DEFAULT 0.0875,
+ tax_amount DECIMAL(10,2) DEFAULT 0.0
+ )
+ """);
+
+ // Tax calculations adapt automatically
+ }
+
+ public void addShippingSupport(IgniteClient client) {
+ // Phase 3: Add regional shipping (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ shipping_region VARCHAR(50) DEFAULT 'DOMESTIC',
+ customs_data VARCHAR(500),
+ estimated_delivery_days INT DEFAULT 3
+ )
+ """);
+ }
+ }
+
+p #[strong Business Benefits:]
+ul
+ li #[strong Continuous Deployment]: Feature releases independent of schema
changes
+ li #[strong A/B Testing]: Test international features with subset of traffic
+ li #[strong Risk Reduction]: Gradual rollout instead of big-bang deployment
+ li #[strong Revenue Protection]: No downtime for existing operations
+
+hr
+br
+
+h2 Schema Evolution Performance Impact
+
+h3 Traditional Schema Change Performance Cost
+
+p #[strong Large Table Schema Changes] (100M+ records):
+
+pre
+ code.
+ -- Traditional ALTER TABLE on 100M records
+ ALTER TABLE orders ADD COLUMN fraud_score DECIMAL(5,2);
+ -- Performance impact:
+ -- - Table lock: 30-60 minutes
+ -- - I/O overhead: Rewrite entire table
+ -- - Replication lag: Hours to catch up
+ -- - Application unavailability: Complete downtime
+
+p #[strong Cost Analysis:]
+ul
+ li #[strong Revenue loss]: $500K-$2M per hour of downtime
+ li #[strong Customer impact]: Service unavailable during business hours
+ li #[strong Engineering cost]: 20+ engineer hours for coordination
+ li #[strong Risk]: Single point of failure for rollback
+
+h3 Apache Ignite Schema Evolution Performance
+
+p #[strong Zero-Downtime Schema Changes] (100M+ records):
+
+p Apache Ignite's catalog-based approach enables rapid schema changes by
updating metadata rather than restructuring data:
+
+p #[strong Performance Characteristics:]
+ul
+ li #[strong Schema change time]: Fast metadata operations (typically under
100ms)
+ li #[strong Application downtime]: Zero
+ li #[strong Throughput impact]: Minimal during change operation
+ li #[strong Recovery time]: Immediate (no recovery needed)
+
+p Performance improves by separating schema metadata management from data
storage, allowing schema evolution without touching existing data structures.
+
+hr
+br
+h2 Business Impact of Schema Flexibility
+
+h3 Revenue Protection
+
+p #[strong E-commerce Platform Example] (processing $10M/month):
+
+p #[strong Traditional Approach:]
+ul
+ li 4 schema changes per year × 3 hours downtime = 12 hours total downtime
+ li Revenue impact: $10M/month ÷ 730 hours/month × 12 hours = $164K lost
annually
+ li Engineering overhead: 80 hours coordination × $150/hour = $12K annually
+ li #[strong Total cost]: $176K annually
+
+p #[strong Apache Ignite Approach:]
+ul
+ li 4 schema changes per year × 0 hours downtime = 0 hours total downtime
+ li Revenue impact: $0 lost
+ li Engineering overhead: 4 hours × $150/hour = $600 annually
+ li #[strong Total cost]: $600 annually
+
+p #[strong Annual savings]: $175K+ (99.7% reduction)
+
+h3 Development Velocity Impact
+
+p #[strong Feature Development Acceleration:]
+
+pre
+ code.
+ // Traditional: Schema change blocks feature development
+ public class TraditionalFeatureDevelopment {
+ // Week 1-2: Plan schema changes across systems
+ // Week 3-4: Coordinate deployment windows
+ // Week 5: Execute schema changes during downtime
+ // Week 6-8: Deploy application changes
+ // Week 9: Validate integration across systems
+
+ // Total: 9 weeks from idea to production
+ }
+ // Apache Ignite: Schema and features evolve together
+ public class FlexibleFeatureDevelopment {
+ public void developFeatureWithSchemaEvolution() {
+ // Day 1: Add required schema fields
+ client.sql().execute(null, "ALTER TABLE customers ADD COLUMN
loyalty_tier VARCHAR(20) DEFAULT 'STANDARD'");
+
+ // Day 1-3: Implement feature logic
+ // Day 4: Deploy to production (no coordination needed)
+ // Day 5: Monitor and iterate
+
+ // Total: 1 week from idea to production
+ }
+ }
+
+p #[strong Impact]: 9x faster feature delivery through schema flexibility.
+
+h3 Competitive Advantage Through Agility
+
+p #[strong Market Response Speed:]
+ul
+ li #[strong Regulatory compliance]: Adapt to new requirements within hours
+ li #[strong Customer feedback]: Implement requested features without
deployment delays
+ li #[strong Competitive pressure]: Launch counter-features without schema
coordination overhead
+
+p #[strong Innovation Capability:]
+ul
+ li #[strong A/B testing]: Try schema variations without impacting production
+ li #[strong Experimentation]: Add telemetry fields for new insights
+ li #[strong Personalization]: Evolve customer data models based on behavior
patterns
+br
+hr
+
+br
+h2 The Operational Evolution Advantage
+
+br
+p Traditional databases force trade-offs between schema stability and
operational agility. Apache Ignite eliminates this trade-off through flexible
schema evolution that supports both operational stability and rapid functional
expansion..
+
+p #[strong The principle]: Your schema should evolve as fast as your business
requirements.
+
+p When market demands shift daily but schema changes occur only during monthly
maintenance windows, the architecture becomes the bottleneck to feature
delivery. Flexible schema evolution ensures the data model advances with
business needs rather than restricting them.
+
+p Fast-paced applications can't afford architectural constraints that slow
adaptation. Schema flexibility becomes a strategic advantage when your system
must evolve faster than competitors can deploy.
+
+hr
+br
+|
+p #[em Return next Tuesday for Part 4, where we explore how integrated
platform performance maintains consistency across all workload types. This
ensures that schema flexibility and business agility don't compromise the
performance characteristics your application requires.]
\ No newline at end of file
diff --git a/blog/apache/index.html
b/blog/apache-ignite-3-architecture-part-3.html
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@@ -3,16 +3,11 @@
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0,
maximum-scale=1" />
- <title>Entries tagged [apache]</title>
- <meta property="og:title" content="Entries tagged [apache]" />
- <link rel="canonical" href="https://ignite.apache.org/blog"; />
- <meta property="og:type" content="article" />
- <meta property="og:url" content="https://ignite.apache.org/blog"; />
- <meta property="og:image" content="/img/og-pic.png" />
+ <title>Apache Ignite Architecture Series: Part 3 - Schema Evolution Under
Operational Pressure: When Downtime Isn't an Option</title>
<link rel="stylesheet"
href="/js/vendor/hystmodal/hystmodal.min.css?ver=0.9" />
<link rel="stylesheet" href="/css/utils.css?ver=0.9" />
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- <link rel="stylesheet" href="/css/blog.css?ver=0.9" />
+ <link rel="stylesheet" href="../css/blog.css?ver=0.9" />
<link rel="stylesheet" href="/css/media.css?ver=0.9" media="only screen
and (max-width:1199px)" />
<link rel="icon" type="image/png" href="/img/favicon.png" />
<!-- Matomo -->
@@ -337,205 +332,387 @@
<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 3 - Schema Evolution Under
Operational Pressure: When Downtime Isn't an Option</h1>
+ <p>
+ December 9, 2025 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 3 - Schema Evolution Under Operational Pressure: When Downtime
Isn't an Option%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-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>
- 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.
- </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 3 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
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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>
- 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.
- </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>,
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Performance, Multi-Language Client Support, and Production
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- </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>,
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- ><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>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/apache-ignite-2-5-scaling.html">Apache
Ignite 2.5: Scaling to 1000s Nodes Clusters</a></h2>
- <div>
- May 31, 2018 by Denis Magda. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-2-5-scaling.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 2.5:
Scaling to 1000s Nodes
Clusters%20https://ignite.apache.org/blog/apache-ignite-2-5-scaling.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Apache Ignite was always appreciated by its users for two
primary things it delivers - scalability and performance. Throughout the
lifetime many distributed systems tend to do performance optimizations from a
release to
- release while making scalability related improvements just a
couple of times. It's not because the scalability is of no interest.
Usually, scalability requirements are set and solved once by a distributed
system and
- don't require significant additional interventions by
engineers.
- </p>
+ <div>
<p>
- However, Apache Ignite grew to the point when the community
decided to revisit its discovery subsystem that influences how well and far
Ignite scales out. The goal was pretty clear - Ignite has to scale to 1000s of
nodes
- as good as it scales to 100s now.
+ 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.
</p>
+ <!-- end -->
<p>
- It took many months to get the task implemented. So, please
join me in welcoming Apache Ignite 2.5 that now can be scaled easily to 1000s
of nodes and goes with other exciting capabilities. Let's check out the
most
- prominent ones.
+ Your high-velocity application runs 24/7. Customer
expectations don't pause for maintenance windows. Functional requirements
evolve continuously. Yet traditional database schema changes require downtime,
coordination
+ across teams, and careful rollback planning.
</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-2-5-scaling.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a href="/blog/apache-ignite-2-4-brings.html">Apache
Ignite 2.4 ings Advanced Machine Learning and Spark DataFrames
Capabilities</a></h2>
- <div>
- March 15, 2018 by Denis Magda. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-2-4-brings.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Apache Ignite 2.4
ings Advanced Machine Learning and Spark DataFrames
Capabilities%20https://ignite.apache.org/blog/apache-ignite-2-4-brings.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- Usually, Ignite community rolls out a new version once in 3
months, but we had to make an exception for Apache Ignite 2.4 that consumed
five months in total. We could easily blame Thanksgiving, Christmas and New Year
- holidays for the delay and would be forgiven, but, in fact,
we were forging the release you can't simply pass by.
- </p>
- <p>Let's dive in and search for a big fish.</p>
- <h3>Machine Learning General Availability</h3>
- <p>
- Eight months ago, at the time of Apache Ignite 2.0, we put
out the first APIs that formed the foundation of the Ignite's machine
learning component of today. Since that time, Ignite machine learning experts
and
- enthusiasts have been moving the liary to the general
availability condition meticulously. And Ignite 2.4 became a milestone that let
us consider the
- <a
href="https://apacheignite.readme.io/docs/machine-learning"; target="_blank">ML
Grid</a> to be production ready.
- </p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/apache-ignite-2-4-brings.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/meltdown-and-spectre-patches-show.html">Meltdown and Spectre
patches show negligible impact to Apache Ignite performance</a></h2>
- <div>
- January 30, 2018 by Denis Magda. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/meltdown-and-spectre-patches-show.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Meltdown and
Spectre patches show negligible impact to Apache Ignite
performance%20https://ignite.apache.org/blog/meltdown-and-spectre-patches-show.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <p>Consider a payment processing system handling peak loads of
50,000 transactions per second. A new compliance rule requires additional
fraud-detection fields. Traditional schema changes would require:</p>
+ <ul>
+ <li><strong>Coordinate downtime</strong> across payment
processing, fraud detection, and reporting systems</li>
+ <li><strong>Apply schema changes</strong> to primary
database, read replicas, and cache layers</li>
+ <li><strong>Deploy application updates</strong> that work
with new schema</li>
+ <li><strong>Validate data consistency</strong> across all
systems</li>
+ </ul>
+ <p><strong>Total downtime</strong>: 2-4 hours. <strong>Lost
revenue</strong>: $500K+ for a payment processor.</p>
+ <p>Apache Ignite 3 eliminates this constraint through flexible
schema evolution. Schema changes apply without downtime, applications adjust
automatically, and system operations continue uninterrupted.</p>
+ <p><strong>The result: operational evolution without
operational interruption.</strong></p>
+ <hr />
+ <br />
+ <h2>The Schema Rigidity Problem at Scale</h2>
+ <h3>Traditional Schema Change Overhead</h3>
+ <p>High-velocity applications face schema evolution challenges
that compound with scale:</p>
+ <p><strong>Multi-System Schema Coordination:</strong></p>
+ <pre class="mermaid">flowchart TB
+ subgraph "Schema Change Impact"
+ Downtime[Planned Downtime<br/>2-4 hours]
+ Revenue[Lost Revenue<br/>$500K+ per hour]
+ Risk[Deployment Risk<br/>Rollback complexity]
+ end
+
+ subgraph "Systems Requiring Updates"
+ Primary[(Primary Database<br/>Schema + Data Migration)]
+ Replica[(Read Replicas<br/>Replication Catch-up)]
+ Cache[(Cache Layer<br/>Schema Invalidation)]
+ Analytics[(Analytics Store<br/>ETL Pipeline Updates)]
+ Apps[Applications<br/>Code + Config Changes]
+ end
+
+ Primary --> Replica
+ Replica --> Cache
+ Cache --> Analytics
+ Analytics --> Apps
+
+ Apps --> Downtime
+ Downtime --> Revenue
+ Revenue --> Risk
+</pre>
+ <p><strong>The Compound Effect:</strong></p>
+ <ul>
+ <li>Each system adds coordination complexity</li>
+ <li>Rollback procedures multiply with system count</li>
+ <li>Testing requires full integration validation</li>
+ <li>Deployment windows must accommodate slowest system</li>
+ </ul>
+ <h3>Real-World Schema Evolution Pressure</h3>
+ <p><strong>E-commerce Platform Evolution</strong> (peak
traffic: 100,000 orders per second):</p>
+ <br />
+ <p><strong>Month 1</strong>: Basic order processing</p>
+ <pre><code>CREATE TABLE orders (
+ order_id BIGINT PRIMARY KEY,
+ customer_id BIGINT,
+ product_id BIGINT,
+ amount DECIMAL(10,2),
+ status VARCHAR(20)
+);
+</code></pre>
+ <br />
+ <p><strong>Month 6</strong>: Add payment processing
compliance</p>
+ <pre><code>-- Traditional approach: downtime required
+ALTER TABLE orders ADD COLUMN payment_method VARCHAR(50);
+ALTER TABLE orders ADD COLUMN payment_processor VARCHAR(30);
+ALTER TABLE orders ADD COLUMN compliance_data VARCHAR(500);
+-- Requires: 2-hour maintenance window
+-- Impact: $200K lost sales during downtime
+</code></pre>
+ <br />
+ <p><strong>Month 12</strong>: International expansion
requirements</p>
+ <pre><code>-- More complexity: multiple system coordination
+ALTER TABLE orders ADD COLUMN currency_code CHAR(3);
+ALTER TABLE orders ADD COLUMN exchange_rate DECIMAL(10,6);
+ALTER TABLE orders ADD COLUMN tax_jurisdiction VARCHAR(50);
+ALTER TABLE orders ADD COLUMN shipping_region VARCHAR(50);
+-- Requires: 4-hour coordinated deployment
+-- Impact: $800K lost sales, customer complaints
+</code></pre>
+ <br />
+ <p><strong>The Pattern</strong>: Each functional change
requires operational disruption that grows with system complexity.</p>
+ <hr />
+ <br />
+ <h2>Apache Ignite Flexible Schema Architecture</h2>
+ <h3>Catalog-Driven Schema Management</h3>
+ <p>Apache Ignite manages schema evolution through versioned
metadata catalogs that coordinate changes across distributed nodes using hybrid
logical clocks for timestamp ordering:</p>
+ <p><strong>Schema Evolution Process:</strong></p>
+ <ul>
+ <li><strong>Validation Phase</strong>: New columns validated
against existing schema</li>
+ <li><strong>Atomic Update</strong>: Schema change applied as
single `NewColumnsEntry` operation</li>
+ <li><strong>Version Management</strong>: Applications
operate against appropriate schema version</li>
+ <li><strong>Cluster Coordination</strong>: HybridTimestamp
ensures consistent activation</li>
+ </ul>
+ <p><strong>Key Advantages:</strong></p>
+ <ul>
+ <li><strong>Atomic Updates</strong>: Schema changes apply as
single operations</li>
+ <li><strong>Version Management</strong>: Applications can
operate against different schema versions</li>
+ <li><strong>Validation</strong>: Automatic conflict
detection and resolution</li>
+ <li><strong>Rollback</strong>: Schema changes are reversible
without data loss</li>
+ </ul>
+ <h3>Time-Based Schema Consistency</h3>
+ <p>Schema changes coordinate across distributed nodes using
cluster-wide timestamps:</p>
+ <p><strong>Consistency Guarantees:</strong></p>
+ <ul>
+ <li><strong>Point-in-Time Activation</strong>: All nodes
apply schema changes simultaneously</li>
+ <li><strong>Transaction Safety</strong>: In-flight
operations complete with original schema</li>
+ <li><strong>Application Compatibility</strong>: Gradual
adoption without breaking changes</li>
+ </ul>
+ <p>The catalog management system uses `HybridTimestamp` values
to ensure schema versions activate consistently across all cluster nodes,
preventing race conditions and maintaining data integrity during schema
evolution.</p>
+ <hr />
+ <br />
+ <h2>Schema Evolution in Production</h2>
+ <h3>Adding Fraud Detection Fields (Real-Time)</h3>
+ <p><strong>Business Requirement</strong>: New fraud detection
requires additional order data fields.</p>
+ <p><strong>Traditional Approach</strong>:</p>
+ <pre><code>-- Requires 2-hour maintenance window
+BEGIN;
+ ALTER TABLE orders ADD COLUMN fraud_score DECIMAL(5,2);
+ ALTER TABLE orders ADD COLUMN risk_factors VARCHAR(500);
+ ALTER TABLE orders ADD COLUMN verification_status VARCHAR(30);
+COMMIT;
+-- Update application code (coordinated deployment)
+-- Update cache schemas (cache invalidation)
+-- Update analytics pipelines (ETL modifications)
+-- Test end-to-end integration</code></pre>
+ <br />
+ <p><strong>Apache Ignite Approach</strong>:</p>
+ <pre><code>// Schema evolution during live operations
+try (IgniteClient client =
IgniteClient.builder().addresses("cluster:10800").build()) {
+ // Add fraud detection columns without downtime
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ fraud_score DECIMAL(5,2) DEFAULT 0.0,
+ risk_factors VARCHAR(500) DEFAULT '',
+ verification_status VARCHAR(30) DEFAULT 'PENDING'
+ )
+ """);
+
+ // Applications immediately see new schema
+ // Existing queries continue working
+ // New functionality can use additional fields
+}
+// Application code adapts automatically
+client.transactions().runInTransaction(tx -> {
+ // Existing order processing continues
+ client.sql().execute(tx, "INSERT INTO orders (order_id, customer_id,
amount) VALUES (?, ?, ?)",
+ orderId, customerId, amount);
+
+ // New fraud detection can use additional fields when ready
+ if (fraudDetectionEnabled) {
+ client.sql().execute(tx, "UPDATE orders SET fraud_score = ?,
risk_factors = ? WHERE order_id = ?",
+ fraudScore, riskFactors, orderId);
+ }
+});
+</code></pre>
+ <p><strong>Result</strong>:</p>
+ <ul>
+ <li><strong>Downtime</strong>: Zero</li>
+ <li><strong>Deployment coordination</strong>: None
required</li>
+ <li><strong>Revenue impact</strong>: Zero</li>
+ <li><strong>Time to production</strong>: Minutes instead of
hours</li>
+ </ul>
+ <h3>Unified Schema Access Across APIs</h3>
+ <p><strong>Here's how the evolved schema works seamlessly
across different access patterns:</strong></p>
+ <pre><code>// The SAME evolved schema accessible through all
APIs immediately
+Table ordersTable = client.tables().table("orders");
+// 1. Key-value access automatically sees new schema
+Tuple orderTuple = ordersTable.keyValueView()
+ .get(tx, Tuple.create().set("order_id", orderId));
+// New fields available: orderTuple.stringValue("verification_status")
+// 2. SQL access uses new fraud detection fields immediately
+ResultSet<SqlRow> suspiciousOrders = client.sql().execute(tx,
+ "SELECT order_id, fraud_score, risk_factors " +
+ "FROM orders WHERE fraud_score > 0.8 AND verification_status = 'REVIEW'");
+// 3. Record access handles new fields through schema evolution
+OrderRecord record = ordersTable.recordView()
+ .get(tx, new OrderRecord(orderId));
+// OrderRecord.fraudScore now available without code changes
+</code></pre>
+ <p><strong>Schema Evolution Benefits:</strong></p>
+ <ul>
+ <li><strong>No API fragmentation</strong>: Same schema
changes work across key-value, SQL, and record APIs</li>
+ <li><strong>No deployment coordination</strong>: All access
patterns see schema changes immediately</li>
+ <li><strong>No data migration</strong>: New fields populate
automatically with defaults</li>
+ <li><strong>No downtime</strong>: Live applications continue
operating during schema evolution</li>
+ </ul>
+ <p><strong>The unified schema advantage</strong>: Schema
changes apply once and work immediately across all data access patterns,
eliminating the multi-system coordination that creates downtime.</p>
+ <h3>International Expansion Schema Evolution</h3>
+ <p><strong>Business Requirement</strong>: Support multiple
currencies and tax jurisdictions.</p>
+ <pre><code>// Progressive schema evolution for international
expansion
+public class InternationalExpansionEvolution {
+
+ public void addCurrencySupport(IgniteClient client) {
+ // Phase 1: Add currency fields (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ currency_code CHAR(3) DEFAULT 'USD',
+ exchange_rate DECIMAL(10,6) DEFAULT 1.0,
+ base_amount DECIMAL(10,2)
+ )
+ """);
+
+ // Applications continue working with existing USD logic
+ // New international orders can specify currency
+ }
+
+ public void addTaxSupport(IgniteClient client) {
+ // Phase 2: Add tax jurisdiction fields (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ tax_jurisdiction VARCHAR(50) DEFAULT 'US-FEDERAL',
+ tax_rate DECIMAL(5,4) DEFAULT 0.0875,
+ tax_amount DECIMAL(10,2) DEFAULT 0.0
+ )
+ """);
+
+ // Tax calculations adapt automatically
+ }
+
+ public void addShippingSupport(IgniteClient client) {
+ // Phase 3: Add regional shipping (no downtime)
+ client.sql().execute(null, """
+ ALTER TABLE orders ADD COLUMN (
+ shipping_region VARCHAR(50) DEFAULT 'DOMESTIC',
+ customs_data VARCHAR(500),
+ estimated_delivery_days INT DEFAULT 3
+ )
+ """);
+ }
+}
+</code></pre>
+ <p><strong>Business Benefits:</strong></p>
+ <ul>
+ <li><strong>Continuous Deployment</strong>: Feature releases
independent of schema changes</li>
+ <li><strong>A/B Testing</strong>: Test international
features with subset of traffic</li>
+ <li><strong>Risk Reduction</strong>: Gradual rollout instead
of big-bang deployment</li>
+ <li><strong>Revenue Protection</strong>: No downtime for
existing operations</li>
+ </ul>
+ <hr />
+ <br />
+ <h2>Schema Evolution Performance Impact</h2>
+ <h3>Traditional Schema Change Performance Cost</h3>
+ <p><strong>Large Table Schema Changes</strong> (100M+
records):</p>
+ <pre><code>-- Traditional ALTER TABLE on 100M records
+ALTER TABLE orders ADD COLUMN fraud_score DECIMAL(5,2);
+-- Performance impact:
+-- - Table lock: 30-60 minutes
+-- - I/O overhead: Rewrite entire table
+-- - Replication lag: Hours to catch up
+-- - Application unavailability: Complete downtime
+</code></pre>
+ <p><strong>Cost Analysis:</strong></p>
+ <ul>
+ <li><strong>Revenue loss</strong>: $500K-$2M per hour of
downtime</li>
+ <li><strong>Customer impact</strong>: Service unavailable
during business hours</li>
+ <li><strong>Engineering cost</strong>: 20+ engineer hours
for coordination</li>
+ <li><strong>Risk</strong>: Single point of failure for
rollback</li>
+ </ul>
+ <h3>Apache Ignite Schema Evolution Performance</h3>
+ <p><strong>Zero-Downtime Schema Changes</strong> (100M+
records):</p>
+ <p>Apache Ignite's catalog-based approach enables rapid schema
changes by updating metadata rather than restructuring data:</p>
+ <p><strong>Performance Characteristics:</strong></p>
+ <ul>
+ <li><strong>Schema change time</strong>: Fast metadata
operations (typically under 100ms)</li>
+ <li><strong>Application downtime</strong>: Zero</li>
+ <li><strong>Throughput impact</strong>: Minimal during
change operation</li>
+ <li><strong>Recovery time</strong>: Immediate (no recovery
needed)</li>
+ </ul>
+ <p>Performance improves by separating schema metadata
management from data storage, allowing schema evolution without touching
existing data structures.</p>
+ <hr />
+ <br />
+ <h2>Business Impact of Schema Flexibility</h2>
+ <h3>Revenue Protection</h3>
+ <p><strong>E-commerce Platform Example</strong> (processing
$10M/month):</p>
+ <p><strong>Traditional Approach:</strong></p>
+ <ul>
+ <li>4 schema changes per year × 3 hours downtime = 12 hours
total downtime</li>
+ <li>Revenue impact: $10M/month ÷ 730 hours/month × 12 hours
= $164K lost annually</li>
+ <li>Engineering overhead: 80 hours coordination × $150/hour
= $12K annually</li>
+ <li><strong>Total cost</strong>: $176K annually</li>
+ </ul>
+ <p><strong>Apache Ignite Approach:</strong></p>
+ <ul>
+ <li>4 schema changes per year × 0 hours downtime = 0 hours
total downtime</li>
+ <li>Revenue impact: $0 lost</li>
+ <li>Engineering overhead: 4 hours × $150/hour = $600
annually</li>
+ <li><strong>Total cost</strong>: $600 annually</li>
+ </ul>
+ <p><strong>Annual savings</strong>: $175K+ (99.7%
reduction)</p>
+ <h3>Development Velocity Impact</h3>
+ <p><strong>Feature Development Acceleration:</strong></p>
+ <pre><code>// Traditional: Schema change blocks feature
development
+public class TraditionalFeatureDevelopment {
+ // Week 1-2: Plan schema changes across systems
+ // Week 3-4: Coordinate deployment windows
+ // Week 5: Execute schema changes during downtime
+ // Week 6-8: Deploy application changes
+ // Week 9: Validate integration across systems
+
+ // Total: 9 weeks from idea to production
+}
+// Apache Ignite: Schema and features evolve together
+public class FlexibleFeatureDevelopment {
+ public void developFeatureWithSchemaEvolution() {
+ // Day 1: Add required schema fields
+ client.sql().execute(null, "ALTER TABLE customers ADD COLUMN
loyalty_tier VARCHAR(20) DEFAULT 'STANDARD'");
+
+ // Day 1-3: Implement feature logic
+ // Day 4: Deploy to production (no coordination needed)
+ // Day 5: Monitor and iterate
+
+ // Total: 1 week from idea to production
+ }
+}
+</code></pre>
+ <p><strong>Impact</strong>: 9x faster feature delivery through
schema flexibility.</p>
+ <h3>Competitive Advantage Through Agility</h3>
+ <p><strong>Market Response Speed:</strong></p>
+ <ul>
+ <li><strong>Regulatory compliance</strong>: Adapt to new
requirements within hours</li>
+ <li><strong>Customer feedback</strong>: Implement requested
features without deployment delays</li>
+ <li><strong>Competitive pressure</strong>: Launch
counter-features without schema coordination overhead</li>
+ </ul>
+ <p><strong>Innovation Capability:</strong></p>
+ <ul>
+ <li><strong>A/B testing</strong>: Try schema variations
without impacting production</li>
+ <li><strong>Experimentation</strong>: Add telemetry fields
for new insights</li>
+ <li><strong>Personalization</strong>: Evolve customer data
models based on behavior patterns</li>
+ </ul>
+ <br />
+ <hr />
+ <br />
+ <h2>The Operational Evolution Advantage</h2>
+ <br />
<p>
- As promised in my <a
href="https://blogs.apache.org/ignite/entry/protecting-apache-ignite-from-meltdown";>initial
blog post</a> on this matter, Apache Ignite community applied security
patches against the
- notorious Meltdown Spectre vulnerabilities and completed
performance testing of general operations and workloads that are typical for
Ignite deployments.
+ Traditional databases force trade-offs between schema
stability and operational agility. Apache Ignite eliminates this trade-off
through flexible schema evolution that supports both operational stability and
rapid
+ functional expansion..
</p>
+ <p><strong>The principle</strong>: Your schema should evolve
as fast as your business requirements.</p>
<p>
- The security patches were applied only for <a
href="https://nvd.nist.gov/vuln/detail/CVE-2017-5754";
target="_blank">CVE-2017-5754</a> (Meltdown) and <a
- href="https://nvd.nist.gov/vuln/detail/CVE-2017-5753";
- target="_blank"
- >CVE-2017-5753</a
- > (Spectre Variant 1) vulnerabilities. The patches
for <a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5715";
target="_blank">CVE-2017-5715</a> (Spectre Variant 2) for the hardware the
community used for
- testing are not stable yet an can
- <a
href="https://newsroom.intel.com/news/root-cause-of-reboot-issue-identified-updated-guidance-for-customers-and-partners/";
target="_blank">cause system reboot issues or another unpredictable
behavior</a>.
+ When market demands shift daily but schema changes occur
only during monthly maintenance windows, the architecture becomes the
bottleneck to feature delivery. Flexible schema evolution ensures the data
model advances with
+ business needs rather than restricting them.
</p>
- <p>The applied patches have shown that the performance
implications are negligible - the performance drop is just in the 0 - 7%
range as the figure shows:</p>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/meltdown-and-spectre-patches-show.html">↓ Read all</a></div>
- </article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/protecting-apache-ignite-from-meltdown.html">Protecting Apache
Ignite from 'Meltdown' and 'Spectre' vulnerabilities</a></h2>
- <div>
- January 8, 2018 by Denis Magda. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/protecting-apache-ignite-from-meltdown.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Protecting Apache
Ignite from 'Meltdown' and 'Spectre'
vulnerabilities%20https://ignite.apache.org/blog/protecting-apache-ignite-from-meltdown.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
+ <p>Fast-paced applications can't afford architectural
constraints that slow adaptation. Schema flexibility becomes a strategic
advantage when your system must evolve faster than competitors can deploy.</p>
+ <hr />
+ <br />
<p>
- The world was rocked after the recent disclosure of the
- <a
href="https://www.vox.com/business-and-finance/2018/1/4/16850004/meltdown-spectre-intel-security-flaw-update";
target="_blank">Meltdown and Spectre</a> vulnerabilities that literally affect
almost all software ever
- developed. Both issues are related to the way all modern
CPUs are designed and this is why they have opened unprecedented security
breaches -- making the software, including Apache Ignite, vulnerable to
- hacker attacks.
+ <em
+ >Return next Tuesday for Part 4, where we explore how
integrated platform performance maintains consistency across all workload
types. This ensures that schema flexibility and business agility don't
compromise the
+ performance characteristics your application requires.</em
+ >
</p>
- <p>The vulnerabilities are registered in the National
Vulnerability Database under the following CVEs:</p>
- <ul>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5753";
target="_blank">CVE-2017-5753</a> — Spectre variant 1</li>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5715";
target="_blank">CVE-2017-5715</a> — Spectre variant 2</li>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5754";
target="_blank">CVE-2017-5754</a> — Meltdown</li>
- </ul>
</div>
- <div class="post__footer"><a class="more"
href="/blog/protecting-apache-ignite-from-meltdown.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>
- </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 48d4154627..c8a484f2dd 100644
--- a/blog/apache/index.html
+++ b/blog/apache/index.html
@@ -341,6 +341,25 @@
<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-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>
+ 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.
+ </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>
@@ -506,30 +525,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/meltdown-and-spectre-patches-show.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/protecting-apache-ignite-from-meltdown.html">Protecting Apache
Ignite from 'Meltdown' and 'Spectre' vulnerabilities</a></h2>
- <div>
- January 8, 2018 by Denis Magda. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/protecting-apache-ignite-from-meltdown.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Protecting Apache
Ignite from 'Meltdown' and 'Spectre'
vulnerabilities%20https://ignite.apache.org/blog/protecting-apache-ignite-from-meltdown.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>
- The world was rocked after the recent disclosure of the
- <a
href="https://www.vox.com/business-and-finance/2018/1/4/16850004/meltdown-spectre-intel-security-flaw-update";
target="_blank">Meltdown and Spectre</a> vulnerabilities that literally affect
almost all software ever
- developed. Both issues are related to the way all modern
CPUs are designed and this is why they have opened unprecedented security
breaches -- making the software, including Apache Ignite, vulnerable to
- hacker attacks.
- </p>
- <p>The vulnerabilities are registered in the National
Vulnerability Database under the following CVEs:</p>
- <ul>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5753";
target="_blank">CVE-2017-5753</a> — Spectre variant 1</li>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5715";
target="_blank">CVE-2017-5715</a> — Spectre variant 2</li>
- <li><a href="https://nvd.nist.gov/vuln/detail/CVE-2017-5754";
target="_blank">CVE-2017-5754</a> — Meltdown</li>
- </ul>
- </div>
- <div class="post__footer"><a class="more"
href="/blog/protecting-apache-ignite-from-meltdown.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 ffd6dd2db6..5c9d0e7281 100644
--- a/blog/ignite/index.html
+++ b/blog/ignite/index.html
@@ -341,6 +341,25 @@
<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-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>
+ 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.
+ </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>
@@ -492,19 +511,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-2-16-0.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-net-dynamic-linq.html">Dynamic LINQ performance and
usability with Ignite.NET and System.Linq.Dynamic</a></h2>
- <div>
- May 22, 2023 by Pavel Tupitsyn. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-net-dynamic-linq.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Dynamic LINQ
performance and usability with Ignite.NET and
System.Linq.Dynamic%20https://ignite.apache.org/blog/apache-ignite-net-dynamic-linq.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>Dynamically building database queries can be necessary for
some use cases, such as UI-defined filtering. This can get challenging with
LINQ frameworks like EF Core and Ignite.NET.</p>
- <p><a
href="https://ptupitsyn.github.io/Dynamic-LINQ-With-Ignite/";>Read
More...</a></p>
- </div>
- </article>
</section>
<section class="blog__footer">
<ul class="pagination">
diff --git a/blog/index.html b/blog/index.html
index 6d61402395..e6ddd5d9f3 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -341,6 +341,25 @@
<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-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>
+ 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.
+ </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>
@@ -492,19 +511,6 @@
</div>
<div class="post__footer"><a class="more"
href="/blog/apache-ignite-2-16-0.html">↓ Read all</a></div>
</article>
- <article class="post">
- <div class="post__header">
- <h2><a
href="/blog/apache-ignite-net-dynamic-linq.html">Dynamic LINQ performance and
usability with Ignite.NET and System.Linq.Dynamic</a></h2>
- <div>
- May 22, 2023 by Pavel Tupitsyn. Share in <a
href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-net-dynamic-linq.html";>Facebook</a><span>,
</span
- ><a href="http://twitter.com/home?status=Dynamic LINQ
performance and usability with Ignite.NET and
System.Linq.Dynamic%20https://ignite.apache.org/blog/apache-ignite-net-dynamic-linq.html";>Twitter</a>
- </div>
- </div>
- <div class="post__content">
- <p>Dynamically building database queries can be necessary for
some use cases, such as UI-defined filtering. This can get challenging with
LINQ frameworks like EF Core and Ignite.NET.</p>
- <p><a
href="https://ptupitsyn.github.io/Dynamic-LINQ-With-Ignite/";>Read
More...</a></p>
- </div>
- </article>
</section>
<section class="blog__footer">
<ul class="pagination">
