refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-apache-ignite
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title: Apache Ignite
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Ignite, In-memory data grid, GridGain]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [in-memory, data-grid, distributed-cache, sql, compute-grid]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: java
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framework: Apache Ignite 2.16
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---
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# Apache Ignite
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## 매 한 줄
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> **"매 distributed in-memory data grid + compute grid + ANSI SQL"**. 매 GridGain (2007) → Apache Ignite (2014, donated). 매 2026 modern stack 은 Ignite 2.16 (GA mid-2025) / Ignite 3.x (preview, 매 new architecture: RAFT-based, ANSI SQL-first, 매 GridGain 9 commercial). 매 Hazelcast / Redis 의 alternative — 매 SQL + ACID transactions 의 differentiator.
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## 매 핵심
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### 매 features
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- **In-memory key-value cache** — partitioned (sharded) or replicated.
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- **Distributed ANSI-99 SQL** — collocated joins, indexes, JDBC/ODBC.
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- **ACID transactions** — pessimistic / optimistic, distributed two-phase commit.
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- **Compute grid** — send code to data (Java/.NET/C++).
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- **Service grid** — deploy stateful services across cluster.
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- **Native persistence** — durable on-disk (since 2.1, 2017).
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- **Streaming** — continuous queries, data streamer.
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### 매 architecture
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- **Topology**: server nodes + client/thin clients.
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- **Affinity**: rendezvous hashing, partition-to-node assignment.
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- **Backup**: synchronous/async backups per cache (RF=N).
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- **Discovery**: TcpDiscoverySpi (multicast / static / Kubernetes / ZooKeeper).
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- **Communication**: TcpCommunicationSpi.
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### 매 응용
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1. **Hot cache layer** — in front of Postgres/Oracle, sub-ms reads.
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2. **Distributed SQL** — operational analytics across shards.
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3. **Compute grid** — financial risk calc, ML feature scoring at data.
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4. **Session storage** — JCache (JSR-107) compliant.
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## 💻 패턴
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### Cache config (Java)
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```java
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IgniteConfiguration cfg = new IgniteConfiguration();
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CacheConfiguration<Long, Order> cc = new CacheConfiguration<>("orders");
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cc.setCacheMode(CacheMode.PARTITIONED);
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cc.setBackups(1);
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cc.setAtomicityMode(CacheAtomicityMode.TRANSACTIONAL);
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cc.setWriteSynchronizationMode(CacheWriteSynchronizationMode.PRIMARY_SYNC);
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cc.setIndexedTypes(Long.class, Order.class);
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cfg.setCacheConfiguration(cc);
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Ignite ignite = Ignition.start(cfg);
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IgniteCache<Long, Order> orders = ignite.cache("orders");
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orders.put(1L, new Order(...));
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```
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### Distributed SQL
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```java
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SqlFieldsQuery q = new SqlFieldsQuery(
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"SELECT o.id, c.name FROM \"orders\".Order o " +
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"JOIN \"customers\".Customer c ON o.customerId = c.id " +
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"WHERE o.status = ?")
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.setArgs("paid");
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try (var cur = orders.query(q)) {
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for (List<?> row : cur) System.out.println(row);
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}
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```
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### Affinity collocation (cross-cache JOIN performance)
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```java
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@QuerySqlField(index = true)
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private Long customerId;
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// Affinity key — rows with same customerId on same node
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@AffinityKeyMapped
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private Long customerId;
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// Now JOIN orders ↔ customers stays node-local
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```
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### Transaction (pessimistic, repeatable read)
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```java
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try (Transaction tx = ignite.transactions().txStart(
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TransactionConcurrency.PESSIMISTIC,
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TransactionIsolation.REPEATABLE_READ)) {
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Account from = accounts.get(fromId);
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Account to = accounts.get(toId);
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if (from.balance < amount) throw new RuntimeException("INSUFFICIENT");
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from.balance -= amount; to.balance += amount;
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accounts.put(fromId, from);
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accounts.put(toId, to);
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tx.commit();
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}
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```
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### Compute grid (broadcast)
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```java
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ignite.compute().broadcast(() -> {
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System.out.println("Hello from " + ignite.cluster().localNode().id());
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});
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// Send Lambda — Ignite peer-class-loads to all nodes
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```
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### Continuous query (CDC-like)
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```java
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ContinuousQuery<Long, Order> qry = new ContinuousQuery<>();
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qry.setLocalListener(events -> {
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for (CacheEntryEvent<? extends Long, ? extends Order> e : events)
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System.out.println("Updated: " + e.getKey() + " → " + e.getValue());
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});
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qry.setRemoteFilterFactory(() -> e -> e.getValue().getStatus().equals("paid"));
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orders.query(qry);
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```
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### Native persistence
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```java
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DataStorageConfiguration ds = new DataStorageConfiguration();
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ds.getDefaultDataRegionConfiguration().setPersistenceEnabled(true);
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ds.setStoragePath("/var/ignite/persistence");
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cfg.setDataStorageConfiguration(ds);
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// Restart-safe; in-memory speed + durability
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ignite.cluster().state(ClusterState.ACTIVE);
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```
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### Thin client (lightweight, no peer-class-loading)
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```java
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ClientConfiguration cc = new ClientConfiguration().setAddresses("ignite:10800");
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try (IgniteClient c = Ignition.startClient(cc)) {
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ClientCache<Long, Order> orders = c.getOrCreateCache("orders");
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orders.put(1L, new Order(...));
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}
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```
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## 매 결정 기준
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| 상황 | Tool |
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|---|---|
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| Pure key-value cache, simple | Redis |
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| K-V + distributed events, JVM | Hazelcast |
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| K-V + ANSI SQL + ACID + compute grid | Ignite |
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| In-process cache | Caffeine |
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| Cloud-native managed | ElastiCache / Memorystore / GridGain Cloud |
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**기본값**: 매 Redis 매 simple cache, 매 Ignite 매 SQL+ACID+compute integrated, 매 Hazelcast 매 JVM-native event-driven.
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## 🔗 Graph
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- 부모: [[In-Memory Data Grid]] · [[Distributed Cache]]
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- 변형: [[GridGain]]
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## 🤖 LLM 활용
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**언제**: 매 sub-ms latency + SQL + ACID 의 simultaneous requirement, 매 compute-near-data, 매 JVM ecosystem.
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**언제 X**: 매 simple cache only (Redis cheaper), 매 non-JVM stack (limited tooling), 매 small data (<10GB, single node fine).
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## ❌ 안티패턴
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- **No backups**: 매 node loss → data loss. 매 setBackups(≥1).
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- **Cross-cache JOIN without affinity**: 매 network shuffle, 매 query 의 slow.
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- **Synchronous replication everywhere**: 매 latency. 매 PRIMARY_SYNC + async backup balance.
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- **Mixing partitioned + replicated joins carelessly**: 매 broadcast amplification.
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## 🧪 검증 / 중복
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- Verified (Apache Ignite docs 2.16, GridGain documentation, ASF Ignite 3.x roadmap).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full content (Ignite cache, SQL, transactions, compute grid) |
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