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new 105d7a6033 IGNITE-27328 Publish part 4 of AI3 architecture blog (#295)
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commit 105d7a6033cad040582858f455970efbb8db670c
Author: jinxxxoid <[email protected]>
AuthorDate: Wed Dec 17 12:53:41 2025 +0400
IGNITE-27328 Publish part 4 of AI3 architecture blog (#295)
---
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blog/apache-ignite-3-architecture-part-4.html | 878 +++++++++++++++++++++
blog/apache/index.html | 16 +
blog/ignite/index.html | 16 +
blog/index.html | 16 +
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+---
+title: "Apache Ignite Architecture Series: Part 4 - Integrated Platform
Performance: Maintaining Speed Under Pressure"
+author: "Michael Aglietti"
+date: 2025-12-16
+tags:
+ - apache
+ - ignite
+---
+
+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.
+
+<!-- end -->
+
+p Financial trades execute in microseconds while risk analytics run
concurrently on the same live data. Compliance reporting processes millions of
transactions without blocking operational processing. This happens through a
unified performance architecture that maintains speed characteristics across
all workload types.
+
+p #[strong Real-time analytics breakthrough: query live transactional data
without ETL delays or performance interference.]
+
+hr
+br
+
+h2 Performance Comparison: Traditional vs Integrated
+
+h3 Traditional Multi-System Performance
+
+p #[strong Concrete performance degradation in financial trading systems:]
+
+p #[strong Resource Competition Example] (Financial Trading System):
+
+pre
+ code.
+ // Traditional system: workloads interfere with each other
+ public class TradingSystemBottlenecks {
+
+ public void processTradeWithInterference() {
+ long tradeStart = System.nanoTime();
+
+ // 1. High-frequency trading (requires <100μs)
+ Trade trade = executeTradeLogic(); // Target: 50μs
+
+ // But system is also running:
+ // - Risk analytics (heavy CPU usage)
+ // - Compliance reporting (heavy I/O)
+ // - Position calculations (memory pressure)
+
+ long actualTime = System.nanoTime() - tradeStart;
+ // Result: 300-2000 microseconds instead of 50 microseconds (6-40x
degradation)
+ }
+ }
+
+p #[strong Performance interference causes:]
+ul
+ li #[strong CPU competition]: Analytics consume CPU needed for microsecond
trading
+ li #[strong Memory competition]: Large queries trigger garbage collection
during trades
+ li #[strong I/O competition]: Batch reports block transaction log writes
+ li #[strong Network competition]: Cross-system data movement affects all
operations
+
+h3 Multi-System Performance Fragmentation
+
+p Separating workloads creates different performance problems:
+
+pre.mermaid.
+ flowchart TB
+ subgraph "Separate Systems"
+ Trading[Trading System<br/>50μs trades<br/>BUT: Stale risk data]
+ Analytics[Analytics System<br/>Fast queries<br/>BUT: Delayed trade
data]
+ Reporting[Reporting System<br/>Complete data<br/>BUT: 10-30min lag]
+ end
+
+ subgraph "Data Movement Overhead"
+ Sync1[Trading → Analytics<br/>2-5ms synchronization]
+ Sync2[Analytics → Reporting<br/>Batch ETL delays]
+ Sync3[Cross-system validation<br/>10-50ms coordination]
+ end
+
+ Trading -->|Real-time feed| Sync1
+ Sync1 --> Analytics
+ Analytics -->|Hourly batch| Sync2
+ Sync2 --> Reporting
+ Reporting -->|Compliance check| Sync3
+ Sync3 --> Trading
+
+p #[strong Performance Trade-off Reality:]
+ul
+ li Fast trading with stale risk calculations (compliance risk)
+ li Fresh analytics with trading delays (performance risk)
+ li Complete reporting with operational lag (business risk)
+
+hr
+br
+
+h2 Apache Ignite Integrated Performance Architecture
+
+h3 Before/After Performance Transformation
+
+p #[strong Traditional: Choose between fast transactions OR real-time
analytics]
+ul
+ li Trading: 50 microseconds per trade (when analytics are OFF)
+ li Analytics: 2-5 second query response (when trading is PAUSED)
+ li Reports: 10-30 minute ETL delay before data availability
+
+p #[strong Integrated Platform: Fast transactions AND real-time analytics
simultaneously]
+ul
+ li Trading: 50 microseconds per trade (while analytics run concurrently)
+ li Analytics: 100-500 milliseconds query response (on live trading data)
+ li Reports: Real-time query results (no ETL delays)
+
+h3 Storage Engine Performance Strategy
+
+p Apache Ignite provides two storage engines that solve different performance
challenges:
+
+p #[strong Memory-Only Storage (aimem)]: Optimized for latency-critical
workloads requiring maximum speed. High-frequency trading operations, real-time
analytics calculations, and session data management benefit from pure memory
operations without persistence overhead. Performance characteristics focus on
microsecond response times.
+
+p #[strong Memory-First Persistence (aipersist)]: Combines memory-speed access
with durability guarantees through asynchronous persistence. Financial
transactions, audit logs, and regulatory data maintain ACID properties while
achieving memory-speed performance. Background checkpointing provides
durability without blocking operations.
+
+p #[strong Storage Engine Benefits:]
+ul
+ li #[strong Performance optimization]: Each engine optimizes for specific
workload characteristics
+ li #[strong Operational flexibility]: Choose durability vs speed based on
data requirements
+ li #[strong Resource efficiency]: Avoid persistence overhead when durability
isn't required
+ li #[strong Mixed workload support]: Different tables use different engines
within the same cluster
+
+h3 Memory Management for Consistent Performance
+
+p Page-based memory management eliminates the serialization overhead and
garbage collection interference that affects performance predictability in
traditional systems:
+
+p #[strong Performance Architecture Benefits:]
+ul
+ li #[strong Predictable access times]: Direct memory operations without GC
interference
+ li #[strong Zero serialization overhead]: Binary operations on native memory
layouts
+ li #[strong Cache efficiency]: Page-based data organization optimizes CPU
cache usage
+ li #[strong Linear memory scaling]: Performance grows directly with
available RAM
+
+p #[strong The Integration Advantage]: Instead of managing memory efficiency
at the application level, the platform handles memory optimization
automatically while maintaining the simple APIs your business logic requires.
+
+h3 Asynchronous API Design for Concurrent Processing
+
+p High-velocity applications require non-blocking operations to maximize
resource utilization:
+
+pre
+ code.
+ // Concurrent processing without blocking
+ public class ConcurrentTradingProcessor {
+
+ public CompletableFuture<TradingResult>
processConcurrentWorkloads(IgniteClient client) {
+ // Execute multiple operations concurrently
+ CompletableFuture<Trade> tradeExecution =
client.transactions().runInTransactionAsync(tx ->
+ client.sql().executeAsync(tx, "INSERT INTO trades VALUES (?,
?, ?)", tradeId, amount, timestamp)
+ );
+
+ CompletableFuture<RiskMetrics> riskCalculation =
client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeId),
+ RiskCalculationJob.class, tradeId
+ );
+
+ CompletableFuture<ComplianceResult> complianceCheck =
client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeId),
+ ComplianceValidationJob.class, tradeId
+ );
+
+ // Combine results when all complete
+ return CompletableFuture.allOf(tradeExecution, riskCalculation,
complianceCheck)
+ .thenApply(v -> new TradingResult(
+ tradeExecution.join(),
+ riskCalculation.join(),
+ complianceCheck.join()
+ ));
+ }
+ }
+
+p #[strong Concurrency Benefits:]
+ul
+ li #[strong Resource utilization]: CPU cores stay busy while I/O completes
+ li #[strong Throughput scaling]: Process multiple operations per thread
+ li #[strong Latency hiding]: Overlapping operations reduce total processing
time
+
+hr
+br
+
+h2 Performance Under Real-World Load Conditions
+
+h3 Performance Under Mixed Workloads
+
+p #[strong High-Frequency Trading Performance:]
+
+p Trade processing achieves sub-microsecond operations through memory-resident
data access. Portfolio validation, risk calculations, and trade execution
happen locally without network calls. Performance scales linearly with
available CPU cores while maintaining consistent latency.
+
+p #[strong Concurrent Analytics Performance:]
+
+p Risk analytics run simultaneously with live trading without mutual
interference. Portfolio analysis queries process the same data that trading
operations update, but analytics access consistent snapshots without blocking
trade execution. Complex SQL aggregations complete while high-frequency trading
continues at full speed.
+
+p #[strong The Performance Integration]: Traditional systems force trade-offs
between transaction speed and analytical capability. Integrated platform
performance eliminates these constraints by supporting both workload types
within the same optimized architecture.
+
+h3 Unified Access Performance Characteristics
+
+p #[strong Here's how the same data maintains optimal performance across
different access patterns:]
+
+pre
+ code.
+ // Trading data accessed through optimal API for each use case
+ Table tradesTable = client.tables().table("trades");
+ // 1. Key-value access for high-frequency lookups
+ Trade trade = tradesTable.keyValueView()
+ .get(tx, Tuple.create().set("trade_id", tradeId)); // <1 ms lookup
+ // 2. SQL access for complex risk analytics
+ ResultSet<SqlRow> riskAnalysis = client.sql().execute(tx,
+ "SELECT account_id, SUM(quantity * price) as exposure " +
+ "FROM trades WHERE trade_date = CURRENT_DATE " +
+ "GROUP BY account_id HAVING exposure > 1000000"); // Parallel
execution
+ // 3. Record access for type-safe transaction processing
+ TradeRecord record = tradesTable.recordView()
+ .get(tx, new TradeRecord(tradeId)); // Type-safe
operations
+
+p #[strong Performance Optimization per Access Pattern:]
+ul
+ li #[strong Key-value operations]: Direct memory access with microsecond
response times
+ li #[strong SQL operations]: Query optimization with parallel execution and
colocation
+ li #[strong Record operations]: Type-safe processing without serialization
overhead
+ li #[strong All operations]: Share same memory-resident data and transaction
guarantees
+
+p #[strong The unified performance advantage]: Each access method optimizes
for its specific use case while operating against the same high-performance
data store. No performance compromises, no data movement overhead.
+
+h3 Compliance Reporting Performance
+
+p #[strong Regulatory Reporting Without Operational Impact:]
+
+p Compliance reporting processes millions of transactions for regulatory
analysis while live trading continues at full speed. Complex aggregation
queries scan large datasets to identify position limit violations, unusual
trading patterns, and risk exposure calculations. These operations complete
without blocking operational processing through concurrent access control.
+
+p #[strong Performance Characteristics:]
+ul
+ li #[strong High data scan rate]: Reports process complete trading datasets
efficiently
+ li #[strong Minimal operational impact]: Compliance queries run concurrently
with live trading
+ li #[strong Memory efficient processing]: Large-scale aggregations use
streaming patterns
+ li #[strong Transactional consistency]: Reports reflect exact point-in-time
data state
+
+h3 Real-Time Analytics Without ETL
+
+p #[strong Live transactional data analytics without traditional ETL delays:]
+
+pre
+ code.
+ // Analytics query running against live trading data
+ // Read-only transaction for snapshot isolation
+ Transaction readOnlyTx = client.transactions().begin(new
TransactionOptions().readOnly(true));
+ ResultSet<SqlRow> liveRiskAnalysis =
client.sql().execute(readOnlyTx, """
+ SELECT
+ account_id,
+ symbol,
+ SUM(quantity * price) as current_exposure,
+ COUNT(*) as trade_count,
+ MAX(timestamp) as last_trade
+ FROM trades
+ WHERE trade_date = CURRENT_DATE
+ AND status = 'EXECUTED'
+ GROUP BY account_id, symbol
+ HAVING current_exposure > 1000000
+ ORDER BY current_exposure DESC
+ """);
+ // This query runs concurrently with:
+ // - Live trading operations inserting new trades
+ // - Risk systems updating positions
+ // - Settlement processes modifying trade status
+ // - All without blocking any operational processing
+
+p #[strong The Real-Time Analytics Breakthrough:]
+ul
+ li #[strong No ETL delays]: Analytics queries run directly against
operational data
+ li #[strong No data staleness]: See trades and positions as they happen, not
hours later
+ li #[strong No system separation]: Same data serves both operations and
analytics
+ li #[strong No performance interference]: Concurrent access prevents
analytics from blocking operations
+
+p #[strong Traditional ETL Problem Solved:]
+
+pre
+ code.
+ // Traditional: Wait for ETL, work with stale data
+ // 1. Trading system writes trades
+ // 2. ETL process extracts (30 min delay)
+ // 3. Analytics warehouse loads (60 min delay)
+ // 4. Dashboard shows 90-minute-old data
+ // Ignite 3: Instant analytics on live data
+ // 1. Trading system writes trades
+ // 2. Analytics queries see data immediately
+ // 3. Dashboard shows real-time positions
+
+p #[strong The integrated approach]: The operational database becomes the
analytics database when designed for both workloads. No more choosing between
fresh data and analytical power.
+
+h3 Intelligent Flow Control Under Extreme Load
+
+p #[strong The system pressure challenge]: During traffic spikes (market
volatility, Black Friday, viral events), traditional systems either drop
connections or crash. Apache Ignite responds intelligently with automatic
backpressure that maintains system stability while preserving data integrity.
+
+pre
+ code.
+ // Intelligent backpressure prevents system collapse during traffic spikes
+ DataStreamerOptions options = DataStreamerOptions.builder()
+ .pageSize(10_000) // Batch size control
+ .perPartitionParallelOperations(4) // Concurrency limits per
partition
+ .autoFlushInterval(1000) // Memory pressure relief (1
second)
+ .retryLimit(32) // Resilience under pressure
+ .build();
+ // Publisher provides intelligent flow control - automatically throttles
when system pressure detected
+ try (var publisher = new
SubmissionPublisherDataStreamerItem<TradeRecord>()) {
+ CompletableFuture<Void> streamerFut =
tradesTable.recordView().streamData(publisher, options);
+
+ // System automatically applies backpressure when memory pressure or
partition limits reached
+ // Instead of dropping connections, Ignite 3 intelligently throttles
input rates
+ publisher.submit(DataStreamerItem.of(tradeRecord));
+
+ streamerFut.join(); // Completes when all data processed with
backpressure applied
+ }
+
+p #[strong Backpressure vs Traditional Approaches:]
+
+pre.mermaid.
+ flowchart LR
+ subgraph "System Under Load"
+ Events1[High Traffic Spike 50,000 events/sec]
+ DB1[(Traditional Database)]
+ Crash1[System Overload 503 Service Unavailable]
+ Loss1[Data Lost Revenue Impact Customer Complaints]
+
+ Events1 -->|Overwhelms| DB1
+ DB1 -->|Cannot Handle Load| Crash1
+ Crash1 --> Loss1
+ end
+
+pre.mermaid.
+ flowchart LR
+ subgraph "Ignite 3 Backpressure"
+ Events2[High Traffic Spike 50,000 events/sec]
+ Ignite[Apache Ignite Backpressure Control]
+ Throttle[Intelligent Throttling Flow Control Active]
+ Success[System Stable Data Preserved 99.9% Uptime]
+
+ Events2 -->|High Load Detected| Ignite
+ Ignite -->|Applies Backpressure| Throttle
+ Throttle -->|Maintains Stability| Success
+ end
+
+p #[strong Traditional Database]: "503 Service Unavailable" → Connections
dropped, data lost
+br
+p #[strong Apache Ignite]: "Intelligent throttling" → System stays up, data
preserved
+
+p #[strong Real-world scenarios where backpressure saves systems:]
+ul
+ li #[strong Market volatility]: 10x normal trading volume handled gracefully
without transaction loss
+ li #[strong IoT sensor bursts]: 50M device readings processed without memory
exhaustion
+ li #[strong E-commerce spikes]: Black Friday traffic managed without dropped
orders
+ li #[strong Data migration]: 100TB+ datasets streamed without overwhelming
target systems
+
+p #[strong The wow moment]: While competitors' systems crash during peak
demand, yours maintains 99.9% uptime through intelligent flow control that
automatically adapts to system pressure.
+
+hr
+br
+
+h2 Performance Optimization Strategies
+
+h3 Workload-Specific Optimization
+
+p #[strong Trading Workload Optimization:]
+
+p High-frequency trading tables use the volatile memory engine (aimem) for
maximum speed. Tables configure colocation by account ID to ensure related
trades process on the same nodes. Distribution zones optimize partition count
and replica settings for trading-specific access patterns.
+
+p #[strong Analytics Workload Optimization:]
+
+p Analytical processing uses the persistent memory engine (aipersist) for
durability with memory-speed access. Market data tables colocate by symbol to
optimize time-series queries. Higher partition counts distribute analytical
workloads across more nodes for better parallelization.
+
+p #[strong Configuration Benefits:]
+ul
+ li #[strong Workload isolation]: Different storage engines prevent
interference between workload types
+ li #[strong Access optimization]: Colocation strategies minimize network
overhead for common query patterns
+ li #[strong Resource utilization]: Optimized partition counts maximize
hardware utilization
+ li #[strong Performance predictability]: Configuration choices align with
specific performance requirements
+
+h3 Performance Monitoring and Validation
+
+p #[strong Continuous Performance Validation:]
+
+p Integrated performance monitoring tracks latency histograms and throughput
gauges across all workload types. The system measures interference patterns
between trading, analytics, and reporting operations to ensure performance
isolation. Automated validation confirms that trading latency remains below
microsecond thresholds while analytical queries maintain their target response
times.
+
+p #[strong Performance Validation Results:]
+ul
+ li #[strong Trading performance]: Sub-microsecond 99.9th percentile latency
under mixed loads
+ li #[strong Analytics performance]: Consistent query response times
regardless of trading volume
+ li #[strong Interference detection]: Less than 5% mutual performance impact
between workload types
+ li #[strong Capacity planning]: Predictable scaling characteristics enable
accurate resource allocation
+
+hr
+br
+
+h2 Business Impact of Consistent Performance
+
+h3 Risk Reduction Through Performance Predictability
+
+p #[strong Financial Risk Mitigation:]
+ul
+ li #[strong Trading execution]: Consistent low-latency execution prevents
slippage
+ li #[strong Risk calculations]: Real-time risk assessment prevents
overexposure
+ li #[strong Compliance monitoring]: Immediate violation detection prevents
penalties
+
+p #[strong Operational Risk Mitigation:]
+ul
+ li #[strong System capacity]: Predictable performance enables accurate
capacity planning
+ li #[strong SLA compliance]: Consistent performance characteristics enable
SLA guarantees
+ li #[strong Incident reduction]: Performance predictability reduces
operational incidents
+
+h3 Revenue Impact of Performance Consistency
+
+p #[strong High-Frequency Trading Firm Benefits:]
+ul
+ li #[strong Execution advantage]: Microsecond latency improvements translate
to competitive advantage
+ li #[strong Risk management]: Real-time risk assessment prevents significant
financial exposure
+ li #[strong Operational efficiency]: Consistent performance reduces manual
intervention needs
+
+p #[strong E-commerce Platform Benefits:]
+ul
+ li #[strong Response time consistency]: Low-latency checkout processes
improve conversion rates
+ li #[strong Analytics availability]: Real-time insights enable rapid revenue
optimization
+ li #[strong System reliability]: High availability during peak load prevents
revenue loss
+
+h3 Competitive Advantage Through Integration
+
+p #[strong Market Differentiation:]
+ul
+ li #[strong Customer experience]: Millisecond response times vs competitor
delays
+ li #[strong Operational agility]: Real-time decision making vs batch
processing delays
+ li #[strong Cost efficiency]: Single platform vs multi-system operational
overhead
+
+p #[strong Innovation Enablement:]
+ul
+ li #[strong New product capabilities]: Performance characteristics enable
previously impossible features
+ li #[strong Market expansion]: Consistent performance supports higher-volume
markets
+ li #[strong Technical differentiation]: Platform capabilities become
competitive advantages
+
+hr
+br
+
+h2 The Performance Integration Advantage
+br
+p Traditional architectures force performance trade-offs between workload
types. Fast operations require dedicated systems. Analytical processing needs
separate infrastructure. Reporting workloads get isolated environments.
+
+p Apache Ignite eliminates these trade-offs through integrated platform
performance. All workload types achieve their required performance
characteristics within the same system, using the same data, without
interference.
+
+p #[strong The principle: Performance consistency enables operational
simplicity.]
+
+p When all workloads perform predictably within the same platform, you
eliminate the operational complexity of managing performance trade-offs across
multiple systems. Your architecture supports business requirements instead of
constraining them.
+
+p High-velocity applications need performance characteristics they can depend
on. Integrated platform performance provides both the speed individual
operations require and the consistency mixed workloads demand.
+
+hr
+br
+|
+p #[em Return next Tuesday for Part 5, that explores how data colocation
eliminates the network overhead that traditional distributed systems accept as
inevitable. This transforms distributed processing into local memory operations
while maintaining the scale and fault tolerance benefits of distributed
architecture.]
\ No newline at end of file
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+ <section class="blog__header post_page__header">
+ <a href="/blog/">← Apache Ignite Blog</a>
+ <h1>Apache Ignite Architecture Series: Part 4 - Integrated Platform
Performance: Maintaining Speed Under Pressure</h1>
+ <p>
+ December 16, 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 4 - Integrated Platform Performance: Maintaining Speed Under
Pressure%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>
+ <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>
+ <!-- end -->
+ <p>
+ Financial trades execute in microseconds while risk
analytics run concurrently on the same live data. Compliance reporting
processes millions of transactions without blocking operational processing.
This happens through a
+ unified performance architecture that maintains speed
characteristics across all workload types.
+ </p>
+ <p><strong>Real-time analytics breakthrough: query live
transactional data without ETL delays or performance interference.</strong></p>
+ <hr />
+ <br />
+ <h2>Performance Comparison: Traditional vs Integrated</h2>
+ <h3>Traditional Multi-System Performance</h3>
+ <p><strong>Concrete performance degradation in financial
trading systems:</strong></p>
+ <p><strong>Resource Competition Example</strong> (Financial
Trading System):</p>
+ <pre><code>// Traditional system: workloads interfere with
each other
+public class TradingSystemBottlenecks {
+
+ public void processTradeWithInterference() {
+ long tradeStart = System.nanoTime();
+
+ // 1. High-frequency trading (requires <100μs)
+ Trade trade = executeTradeLogic(); // Target: 50μs
+
+ // But system is also running:
+ // - Risk analytics (heavy CPU usage)
+ // - Compliance reporting (heavy I/O)
+ // - Position calculations (memory pressure)
+
+ long actualTime = System.nanoTime() - tradeStart;
+ // Result: 300-2000 microseconds instead of 50 microseconds (6-40x
degradation)
+ }
+}
+</code></pre>
+ <p><strong>Performance interference causes:</strong></p>
+ <ul>
+ <li><strong>CPU competition</strong>: Analytics consume CPU
needed for microsecond trading</li>
+ <li><strong>Memory competition</strong>: Large queries
trigger garbage collection during trades</li>
+ <li><strong>I/O competition</strong>: Batch reports block
transaction log writes</li>
+ <li><strong>Network competition</strong>: Cross-system data
movement affects all operations</li>
+ </ul>
+ <h3>Multi-System Performance Fragmentation</h3>
+ <p>Separating workloads creates different performance
problems:</p>
+ <pre class="mermaid">flowchart TB
+ subgraph "Separate Systems"
+ Trading[Trading System<br/>50μs trades<br/>BUT: Stale risk data]
+ Analytics[Analytics System<br/>Fast queries<br/>BUT: Delayed trade
data]
+ Reporting[Reporting System<br/>Complete data<br/>BUT: 10-30min lag]
+ end
+
+ subgraph "Data Movement Overhead"
+ Sync1[Trading → Analytics<br/>2-5ms synchronization]
+ Sync2[Analytics → Reporting<br/>Batch ETL delays]
+ Sync3[Cross-system validation<br/>10-50ms coordination]
+ end
+
+ Trading -->|Real-time feed| Sync1
+ Sync1 --> Analytics
+ Analytics -->|Hourly batch| Sync2
+ Sync2 --> Reporting
+ Reporting -->|Compliance check| Sync3
+ Sync3 --> Trading
+</pre>
+ <p><strong>Performance Trade-off Reality:</strong></p>
+ <ul>
+ <li>Fast trading with stale risk calculations (compliance
risk)</li>
+ <li>Fresh analytics with trading delays (performance
risk)</li>
+ <li>Complete reporting with operational lag (business
risk)</li>
+ </ul>
+ <hr />
+ <br />
+ <h2>Apache Ignite Integrated Performance Architecture</h2>
+ <h3>Before/After Performance Transformation</h3>
+ <p><strong>Traditional: Choose between fast transactions OR
real-time analytics</strong></p>
+ <ul>
+ <li>Trading: 50 microseconds per trade (when analytics are
OFF)</li>
+ <li>Analytics: 2-5 second query response (when trading is
PAUSED)</li>
+ <li>Reports: 10-30 minute ETL delay before data
availability</li>
+ </ul>
+ <p><strong>Integrated Platform: Fast transactions AND
real-time analytics simultaneously</strong></p>
+ <ul>
+ <li>Trading: 50 microseconds per trade (while analytics run
concurrently)</li>
+ <li>Analytics: 100-500 milliseconds query response (on live
trading data)</li>
+ <li>Reports: Real-time query results (no ETL delays)</li>
+ </ul>
+ <h3>Storage Engine Performance Strategy</h3>
+ <p>Apache Ignite provides two storage engines that solve
different performance challenges:</p>
+ <p>
+ <strong>Memory-Only Storage (aimem)</strong>: Optimized for
latency-critical workloads requiring maximum speed. High-frequency trading
operations, real-time analytics calculations, and session data management
benefit from
+ pure memory operations without persistence overhead.
Performance characteristics focus on microsecond response times.
+ </p>
+ <p>
+ <strong>Memory-First Persistence (aipersist)</strong>:
Combines memory-speed access with durability guarantees through asynchronous
persistence. Financial transactions, audit logs, and regulatory data maintain
ACID
+ properties while achieving memory-speed performance.
Background checkpointing provides durability without blocking operations.
+ </p>
+ <p><strong>Storage Engine Benefits:</strong></p>
+ <ul>
+ <li><strong>Performance optimization</strong>: Each engine
optimizes for specific workload characteristics</li>
+ <li><strong>Operational flexibility</strong>: Choose
durability vs speed based on data requirements</li>
+ <li><strong>Resource efficiency</strong>: Avoid persistence
overhead when durability isn't required</li>
+ <li><strong>Mixed workload support</strong>: Different
tables use different engines within the same cluster</li>
+ </ul>
+ <h3>Memory Management for Consistent Performance</h3>
+ <p>Page-based memory management eliminates the serialization
overhead and garbage collection interference that affects performance
predictability in traditional systems:</p>
+ <p><strong>Performance Architecture Benefits:</strong></p>
+ <ul>
+ <li><strong>Predictable access times</strong>: Direct memory
operations without GC interference</li>
+ <li><strong>Zero serialization overhead</strong>: Binary
operations on native memory layouts</li>
+ <li><strong>Cache efficiency</strong>: Page-based data
organization optimizes CPU cache usage</li>
+ <li><strong>Linear memory scaling</strong>: Performance
grows directly with available RAM</li>
+ </ul>
+ <p>
+ <strong>The Integration Advantage</strong>: Instead of
managing memory efficiency at the application level, the platform handles
memory optimization automatically while maintaining the simple APIs your
business logic
+ requires.
+ </p>
+ <h3>Asynchronous API Design for Concurrent Processing</h3>
+ <p>High-velocity applications require non-blocking operations
to maximize resource utilization:</p>
+ <pre><code>// Concurrent processing without blocking
+public class ConcurrentTradingProcessor {
+
+ public CompletableFuture<TradingResult>
processConcurrentWorkloads(IgniteClient client) {
+ // Execute multiple operations concurrently
+ CompletableFuture<Trade> tradeExecution =
client.transactions().runInTransactionAsync(tx ->
+ client.sql().executeAsync(tx, "INSERT INTO trades VALUES (?, ?,
?)", tradeId, amount, timestamp)
+ );
+
+ CompletableFuture<RiskMetrics> riskCalculation =
client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeId),
+ RiskCalculationJob.class, tradeId
+ );
+
+ CompletableFuture<ComplianceResult> complianceCheck =
client.compute().executeAsync(
+ JobTarget.colocated("trades", tradeId),
+ ComplianceValidationJob.class, tradeId
+ );
+
+ // Combine results when all complete
+ return CompletableFuture.allOf(tradeExecution, riskCalculation,
complianceCheck)
+ .thenApply(v -> new TradingResult(
+ tradeExecution.join(),
+ riskCalculation.join(),
+ complianceCheck.join()
+ ));
+ }
+}
+</code></pre>
+ <p><strong>Concurrency Benefits:</strong></p>
+ <ul>
+ <li><strong>Resource utilization</strong>: CPU cores stay
busy while I/O completes</li>
+ <li><strong>Throughput scaling</strong>: Process multiple
operations per thread</li>
+ <li><strong>Latency hiding</strong>: Overlapping operations
reduce total processing time</li>
+ </ul>
+ <hr />
+ <br />
+ <h2>Performance Under Real-World Load Conditions</h2>
+ <h3>Performance Under Mixed Workloads</h3>
+ <p><strong>High-Frequency Trading Performance:</strong></p>
+ <p>
+ Trade processing achieves sub-microsecond operations through
memory-resident data access. Portfolio validation, risk calculations, and trade
execution happen locally without network calls. Performance scales linearly with
+ available CPU cores while maintaining consistent latency.
+ </p>
+ <p><strong>Concurrent Analytics Performance:</strong></p>
+ <p>
+ Risk analytics run simultaneously with live trading without
mutual interference. Portfolio analysis queries process the same data that
trading operations update, but analytics access consistent snapshots without
blocking
+ trade execution. Complex SQL aggregations complete while
high-frequency trading continues at full speed.
+ </p>
+ <p>
+ <strong>The Performance Integration</strong>: Traditional
systems force trade-offs between transaction speed and analytical capability.
Integrated platform performance eliminates these constraints by supporting both
+ workload types within the same optimized architecture.
+ </p>
+ <h3>Unified Access Performance Characteristics</h3>
+ <p><strong>Here's how the same data maintains optimal
performance across different access patterns:</strong></p>
+ <pre><code>// Trading data accessed through optimal API for
each use case
+Table tradesTable = client.tables().table("trades");
+// 1. Key-value access for high-frequency lookups
+Trade trade = tradesTable.keyValueView()
+ .get(tx, Tuple.create().set("trade_id", tradeId)); // <1 ms lookup
+// 2. SQL access for complex risk analytics
+ResultSet<SqlRow> riskAnalysis = client.sql().execute(tx,
+ "SELECT account_id, SUM(quantity * price) as exposure " +
+ "FROM trades WHERE trade_date = CURRENT_DATE " +
+ "GROUP BY account_id HAVING exposure > 1000000"); // Parallel
execution
+// 3. Record access for type-safe transaction processing
+TradeRecord record = tradesTable.recordView()
+ .get(tx, new TradeRecord(tradeId)); // Type-safe
operations
+</code></pre>
+ <p><strong>Performance Optimization per Access
Pattern:</strong></p>
+ <ul>
+ <li><strong>Key-value operations</strong>: Direct memory
access with microsecond response times</li>
+ <li><strong>SQL operations</strong>: Query optimization with
parallel execution and colocation</li>
+ <li><strong>Record operations</strong>: Type-safe processing
without serialization overhead</li>
+ <li><strong>All operations</strong>: Share same
memory-resident data and transaction guarantees</li>
+ </ul>
+ <p>
+ <strong>The unified performance advantage</strong>: Each
access method optimizes for its specific use case while operating against the
same high-performance data store. No performance compromises, no data movement
+ overhead.
+ </p>
+ <h3>Compliance Reporting Performance</h3>
+ <p><strong>Regulatory Reporting Without Operational
Impact:</strong></p>
+ <p>
+ Compliance reporting processes millions of transactions for
regulatory analysis while live trading continues at full speed. Complex
aggregation queries scan large datasets to identify position limit violations,
unusual
+ trading patterns, and risk exposure calculations. These
operations complete without blocking operational processing through concurrent
access control.
+ </p>
+ <p><strong>Performance Characteristics:</strong></p>
+ <ul>
+ <li><strong>High data scan rate</strong>: Reports process
complete trading datasets efficiently</li>
+ <li><strong>Minimal operational impact</strong>: Compliance
queries run concurrently with live trading</li>
+ <li><strong>Memory efficient processing</strong>:
Large-scale aggregations use streaming patterns</li>
+ <li><strong>Transactional consistency</strong>: Reports
reflect exact point-in-time data state</li>
+ </ul>
+ <h3>Real-Time Analytics Without ETL</h3>
+ <p><strong>Live transactional data analytics without
traditional ETL delays:</strong></p>
+ <pre><code>// Analytics query running against live trading data
+// Read-only transaction for snapshot isolation
+Transaction readOnlyTx = client.transactions().begin(new
TransactionOptions().readOnly(true));
+ResultSet<SqlRow> liveRiskAnalysis = client.sql().execute(readOnlyTx, """
+ SELECT
+ account_id,
+ symbol,
+ SUM(quantity * price) as current_exposure,
+ COUNT(*) as trade_count,
+ MAX(timestamp) as last_trade
+ FROM trades
+ WHERE trade_date = CURRENT_DATE
+ AND status = 'EXECUTED'
+ GROUP BY account_id, symbol
+ HAVING current_exposure > 1000000
+ ORDER BY current_exposure DESC
+""");
+// This query runs concurrently with:
+// - Live trading operations inserting new trades
+// - Risk systems updating positions
+// - Settlement processes modifying trade status
+// - All without blocking any operational processing
+</code></pre>
+ <p><strong>The Real-Time Analytics Breakthrough:</strong></p>
+ <ul>
+ <li><strong>No ETL delays</strong>: Analytics queries run
directly against operational data</li>
+ <li><strong>No data staleness</strong>: See trades and
positions as they happen, not hours later</li>
+ <li><strong>No system separation</strong>: Same data serves
both operations and analytics</li>
+ <li><strong>No performance interference</strong>: Concurrent
access prevents analytics from blocking operations</li>
+ </ul>
+ <p><strong>Traditional ETL Problem Solved:</strong></p>
+ <pre><code>// Traditional: Wait for ETL, work with stale data
+// 1. Trading system writes trades
+// 2. ETL process extracts (30 min delay)
+// 3. Analytics warehouse loads (60 min delay)
+// 4. Dashboard shows 90-minute-old data
+// Ignite 3: Instant analytics on live data
+// 1. Trading system writes trades
+// 2. Analytics queries see data immediately
+// 3. Dashboard shows real-time positions
+</code></pre>
+ <p><strong>The integrated approach</strong>: The operational
database becomes the analytics database when designed for both workloads. No
more choosing between fresh data and analytical power.</p>
+ <h3>Intelligent Flow Control Under Extreme Load</h3>
+ <p>
+ <strong>The system pressure challenge</strong>: During
traffic spikes (market volatility, Black Friday, viral events), traditional
systems either drop connections or crash. Apache Ignite responds intelligently
with
+ automatic backpressure that maintains system stability while
preserving data integrity.
+ </p>
+ <pre><code>// Intelligent backpressure prevents system
collapse during traffic spikes
+DataStreamerOptions options = DataStreamerOptions.builder()
+ .pageSize(10_000) // Batch size control
+ .perPartitionParallelOperations(4) // Concurrency limits per partition
+ .autoFlushInterval(1000) // Memory pressure relief (1 second)
+ .retryLimit(32) // Resilience under pressure
+ .build();
+// Publisher provides intelligent flow control - automatically throttles when
system pressure detected
+try (var publisher = new
SubmissionPublisherDataStreamerItem<TradeRecord>()) {
+ CompletableFuture<Void> streamerFut =
tradesTable.recordView().streamData(publisher, options);
+
+ // System automatically applies backpressure when memory pressure or
partition limits reached
+ // Instead of dropping connections, Ignite 3 intelligently throttles input
rates
+ publisher.submit(DataStreamerItem.of(tradeRecord));
+
+ streamerFut.join(); // Completes when all data processed with backpressure
applied
+}
+</code></pre>
+ <p><strong>Backpressure vs Traditional Approaches:</strong></p>
+ <pre class="mermaid">
+flowchart LR
+ subgraph "System Under Load"
+ Events1[High Traffic Spike 50,000 events/sec]
+ DB1[(Traditional Database)]
+ Crash1[System Overload 503 Service Unavailable]
+ Loss1[Data Lost Revenue Impact Customer Complaints]
+
+ Events1 -->|Overwhelms| DB1
+ DB1 -->|Cannot Handle Load| Crash1
+ Crash1 --> Loss1
+ end
+</pre
+ >
+ <pre class="mermaid">
+flowchart LR
+ subgraph "Ignite 3 Backpressure"
+ Events2[High Traffic Spike 50,000 events/sec]
+ Ignite[Apache Ignite Backpressure Control]
+ Throttle[Intelligent Throttling Flow Control Active]
+ Success[System Stable Data Preserved 99.9% Uptime]
+
+ Events2 -->|High Load Detected| Ignite
+ Ignite -->|Applies Backpressure| Throttle
+ Throttle -->|Maintains Stability| Success
+ end
+</pre
+ >
+ <p><strong>Traditional Database</strong>: "503 Service
Unavailable" → Connections dropped, data lost</p>
+ <br />
+ <p><strong>Apache Ignite</strong>: "Intelligent throttling" →
System stays up, data preserved</p>
+ <p><strong>Real-world scenarios where backpressure saves
systems:</strong></p>
+ <ul>
+ <li><strong>Market volatility</strong>: 10x normal trading
volume handled gracefully without transaction loss</li>
+ <li><strong>IoT sensor bursts</strong>: 50M device readings
processed without memory exhaustion</li>
+ <li><strong>E-commerce spikes</strong>: Black Friday traffic
managed without dropped orders</li>
+ <li><strong>Data migration</strong>: 100TB+ datasets
streamed without overwhelming target systems</li>
+ </ul>
+ <p><strong>The wow moment</strong>: While competitors' systems
crash during peak demand, yours maintains 99.9% uptime through intelligent flow
control that automatically adapts to system pressure.</p>
+ <hr />
+ <br />
+ <h2>Performance Optimization Strategies</h2>
+ <h3>Workload-Specific Optimization</h3>
+ <p><strong>Trading Workload Optimization:</strong></p>
+ <p>
+ High-frequency trading tables use the volatile memory engine
(aimem) for maximum speed. Tables configure colocation by account ID to ensure
related trades process on the same nodes. Distribution zones optimize partition
+ count and replica settings for trading-specific access
patterns.
+ </p>
+ <p><strong>Analytics Workload Optimization:</strong></p>
+ <p>
+ Analytical processing uses the persistent memory engine
(aipersist) for durability with memory-speed access. Market data tables
colocate by symbol to optimize time-series queries. Higher partition counts
distribute
+ analytical workloads across more nodes for better
parallelization.
+ </p>
+ <p><strong>Configuration Benefits:</strong></p>
+ <ul>
+ <li><strong>Workload isolation</strong>: Different storage
engines prevent interference between workload types</li>
+ <li><strong>Access optimization</strong>: Colocation
strategies minimize network overhead for common query patterns</li>
+ <li><strong>Resource utilization</strong>: Optimized
partition counts maximize hardware utilization</li>
+ <li><strong>Performance predictability</strong>:
Configuration choices align with specific performance requirements</li>
+ </ul>
+ <h3>Performance Monitoring and Validation</h3>
+ <p><strong>Continuous Performance Validation:</strong></p>
+ <p>
+ Integrated performance monitoring tracks latency histograms
and throughput gauges across all workload types. The system measures
interference patterns between trading, analytics, and reporting operations to
ensure
+ performance isolation. Automated validation confirms that
trading latency remains below microsecond thresholds while analytical queries
maintain their target response times.
+ </p>
+ <p><strong>Performance Validation Results:</strong></p>
+ <ul>
+ <li><strong>Trading performance</strong>: Sub-microsecond
99.9th percentile latency under mixed loads</li>
+ <li><strong>Analytics performance</strong>: Consistent query
response times regardless of trading volume</li>
+ <li><strong>Interference detection</strong>: Less than 5%
mutual performance impact between workload types</li>
+ <li><strong>Capacity planning</strong>: Predictable scaling
characteristics enable accurate resource allocation</li>
+ </ul>
+ <hr />
+ <br />
+ <h2>Business Impact of Consistent Performance</h2>
+ <h3>Risk Reduction Through Performance Predictability</h3>
+ <p><strong>Financial Risk Mitigation:</strong></p>
+ <ul>
+ <li><strong>Trading execution</strong>: Consistent
low-latency execution prevents slippage</li>
+ <li><strong>Risk calculations</strong>: Real-time risk
assessment prevents overexposure</li>
+ <li><strong>Compliance monitoring</strong>: Immediate
violation detection prevents penalties</li>
+ </ul>
+ <p><strong>Operational Risk Mitigation:</strong></p>
+ <ul>
+ <li><strong>System capacity</strong>: Predictable
performance enables accurate capacity planning</li>
+ <li><strong>SLA compliance</strong>: Consistent performance
characteristics enable SLA guarantees</li>
+ <li><strong>Incident reduction</strong>: Performance
predictability reduces operational incidents</li>
+ </ul>
+ <h3>Revenue Impact of Performance Consistency</h3>
+ <p><strong>High-Frequency Trading Firm Benefits:</strong></p>
+ <ul>
+ <li><strong>Execution advantage</strong>: Microsecond
latency improvements translate to competitive advantage</li>
+ <li><strong>Risk management</strong>: Real-time risk
assessment prevents significant financial exposure</li>
+ <li><strong>Operational efficiency</strong>: Consistent
performance reduces manual intervention needs</li>
+ </ul>
+ <p><strong>E-commerce Platform Benefits:</strong></p>
+ <ul>
+ <li><strong>Response time consistency</strong>: Low-latency
checkout processes improve conversion rates</li>
+ <li><strong>Analytics availability</strong>: Real-time
insights enable rapid revenue optimization</li>
+ <li><strong>System reliability</strong>: High availability
during peak load prevents revenue loss</li>
+ </ul>
+ <h3>Competitive Advantage Through Integration</h3>
+ <p><strong>Market Differentiation:</strong></p>
+ <ul>
+ <li><strong>Customer experience</strong>: Millisecond
response times vs competitor delays</li>
+ <li><strong>Operational agility</strong>: Real-time decision
making vs batch processing delays</li>
+ <li><strong>Cost efficiency</strong>: Single platform vs
multi-system operational overhead</li>
+ </ul>
+ <p><strong>Innovation Enablement:</strong></p>
+ <ul>
+ <li><strong>New product capabilities</strong>: Performance
characteristics enable previously impossible features</li>
+ <li><strong>Market expansion</strong>: Consistent
performance supports higher-volume markets</li>
+ <li><strong>Technical differentiation</strong>: Platform
capabilities become competitive advantages</li>
+ </ul>
+ <hr />
+ <br />
+ <h2>The Performance Integration Advantage</h2>
+ <br />
+ <p>
+ Traditional architectures force performance trade-offs
between workload types. Fast operations require dedicated systems. Analytical
processing needs separate infrastructure. Reporting workloads get isolated
environments.
+ </p>
+ <p>
+ Apache Ignite eliminates these trade-offs through integrated
platform performance. All workload types achieve their required performance
characteristics within the same system, using the same data, without
interference.
+ </p>
+ <p><strong>The principle: Performance consistency enables
operational simplicity.</strong></p>
+ <p>
+ When all workloads perform predictably within the same
platform, you eliminate the operational complexity of managing performance
trade-offs across multiple systems. Your architecture supports business
requirements instead
+ of constraining them.
+ </p>
+ <p>High-velocity applications need performance characteristics
they can depend on. Integrated platform performance provides both the speed
individual operations require and the consistency mixed workloads demand.</p>
+ <hr />
+ <br />
+ <p>
+ <em
+ >Return next Tuesday for Part 5, that explores how data
colocation eliminates the network overhead that traditional distributed systems
accept as inevitable. This transforms distributed processing into local memory
+ operations while maintaining the scale and fault tolerance
benefits of distributed architecture.</em
+ >
+ </p>
+ </div>
+ </article>
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diff --git a/blog/apache/index.html b/blog/apache/index.html
index c3dc531734..8039bb6d3b 100644
--- a/blog/apache/index.html
+++ b/blog/apache/index.html
@@ -341,6 +341,22 @@
<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-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>
+ </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>
diff --git a/blog/ignite/index.html b/blog/ignite/index.html
index 77011acf46..2ef5035245 100644
--- a/blog/ignite/index.html
+++ b/blog/ignite/index.html
@@ -341,6 +341,22 @@
<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-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>
+ </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>
diff --git a/blog/index.html b/blog/index.html
index 38eedb25fe..5f720d7cae 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -352,6 +352,22 @@
<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-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>
+ </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>
