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Antigravity Agent
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id: wiki-2026-0508-in-memory-data-grid
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title: In-Memory Data Grid
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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: [IMDG, Distributed Cache, In-Memory Computing]
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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: [architecture, distributed-systems, cache, performance, jvm]
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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: hazelcast-ignite
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---
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# In-Memory Data Grid
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## 매 한 줄
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> **"매 distributed RAM 의 partitioned + replicated 의 통한 sub-ms key-value + compute 의 horizontal scale"**. 매 Oracle Coherence (2001) 의 commercial origin, 매 Hazelcast (2008) / Apache Ignite (2014) 의 OSS 의 popularization — 매 modern 의 Redis Cluster + Apache Ignite + Hazelcast 5.x 의 dominant.
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## 매 핵심
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### 매 IMDG vs Distributed Cache
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- **Cache (Redis, Memcached)**: 매 read-through, eviction-driven, simple K/V.
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- **IMDG (Hazelcast, Ignite, Coherence)**: + co-located compute, SQL, transactions, near-cache, entry processors, CP subsystem.
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### 매 Core Capabilities
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- **Partitioning**: consistent hash, 매 271 partitions (Hazelcast default).
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- **Replication**: backup count (sync/async), 매 partition 의 N-1 backup.
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- **Near Cache**: client-side mirror, invalidation 의 push.
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- **Entry Processor**: 매 data-local computation (move code to data).
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- **Continuous Query**: predicate-based push.
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- **CP Subsystem**: Raft-based linearizable primitives (Hazelcast 4+).
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### 매 응용
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1. Session store / shopping cart (low-latency).
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2. Real-time risk / pricing (compute grid).
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3. Hybrid OLTP+stream (Ignite + Kafka).
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## 💻 패턴
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### Hazelcast 5 — Embedded + IMap
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```java
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HazelcastInstance hz = Hazelcast.newHazelcastInstance();
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IMap<String, Order> orders = hz.getMap("orders");
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orders.put("o-123", new Order(...));
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Order o = orders.get("o-123"); // sub-ms
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// Pessimistic lock 의 partition-local
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orders.executeOnKey("o-123", entry -> {
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Order cur = entry.getValue();
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cur.markPaid();
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entry.setValue(cur);
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return null;
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});
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```
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### Apache Ignite — SQL over Cache
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```java
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IgniteConfiguration cfg = new IgniteConfiguration();
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Ignite ignite = Ignition.start(cfg);
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CacheConfiguration<Long, Person> ccfg = new CacheConfiguration<>("Person");
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ccfg.setIndexedTypes(Long.class, Person.class);
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IgniteCache<Long, Person> cache = ignite.getOrCreateCache(ccfg);
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List<List<?>> rows = cache.query(new SqlFieldsQuery(
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"SELECT name, salary FROM Person WHERE salary > ? ORDER BY salary DESC")
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.setArgs(100_000)).getAll();
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```
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### Hazelcast Near Cache
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```yaml
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hazelcast-client:
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near-cache:
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orders:
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in-memory-format: OBJECT
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invalidate-on-change: true
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time-to-live-seconds: 60
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max-size: 10000
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eviction-policy: LRU
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```
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### Entry Processor (move compute to data)
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```java
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public class IncrementVersion implements EntryProcessor<String, Order, Long> {
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public Long process(Map.Entry<String, Order> e) {
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Order o = e.getValue();
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o.setVersion(o.getVersion() + 1);
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e.setValue(o);
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return o.getVersion();
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}
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}
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Long v = orders.executeOnKey("o-1", new IncrementVersion());
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```
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### Continuous Query (Hazelcast)
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```java
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IMap<String, Order> orders = hz.getMap("orders");
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orders.addEntryListener((EntryAddedListener<String, Order>) ev -> {
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if (ev.getValue().getAmount() > 10_000) alert(ev.getValue());
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}, Predicates.greaterThan("amount", 10_000), true);
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```
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### CP Subsystem — Linearizable Counter
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```java
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CPSubsystem cp = hz.getCPSubsystem();
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IAtomicLong seq = cp.getAtomicLong("order-seq");
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long next = seq.incrementAndGet(); // Raft-backed, linearizable
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```
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### Kubernetes Deployment (Hazelcast Operator)
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```yaml
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apiVersion: hazelcast.com/v1alpha1
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kind: Hazelcast
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metadata: { name: hz }
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spec:
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clusterSize: 5
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repository: hazelcast/hazelcast
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version: "5.5"
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persistence:
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baseDir: /data/hot-restart
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pvc: { accessModes: [ReadWriteOnce], requestStorage: 50Gi }
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Simple K/V cache | Redis / Memcached |
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| Java-heavy + SQL on cache | Apache Ignite |
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| Compute + cache + CP primitives | Hazelcast 5 |
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| Multi-language polyglot | Redis Cluster + redis-om |
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| Persistent in-memory DB | Ignite native persistence / Aerospike |
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**기본값**: 매 Java/Kotlin stack — Hazelcast 5; 매 polyglot — Redis Cluster.
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## 🔗 Graph
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- 부모: [[Distributed-Systems]]
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- 변형: [[Distributed-Cache]] · [[NewSQL]]
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- 응용: [[Apache Ignite]]
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- Adjacent: [[CAP-Theorem]] · [[Consistent-Hashing]]
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## 🤖 LLM 활용
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**언제**: IMDG 의 sizing 의 estimate, partition strategy 의 review, Hazelcast/Ignite config 의 generate.
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**언제 X**: 매 production 의 capacity planning 의 final sign-off (real workload benchmark 필수).
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## ❌ 안티패턴
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- **Distributed monolith state**: 매 service 의 IMDG 의 shared mutable state — 매 hidden coupling.
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- **N+1 across grid**: client-side loop 의 단일 키 fetch — 매 batch API 의 use.
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- **No backup count**: backup=0 → 매 node loss 의 data loss.
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- **Serialization neglect**: default Java serialization → 매 slow + bloated, 매 IdentifiedDataSerializable / Compact 의 use.
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- **Treating IMDG as durable DB**: 매 persistence 의 explicit config 없이 → restart 의 data loss.
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## 🧪 검증 / 중복
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- Verified (Hazelcast 5.5 docs, Apache Ignite 2.16 docs, Oracle Coherence 14c docs).
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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 — IMDG (Hazelcast/Ignite) 의 full content |
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