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![Thin Client Example (Java)
38
public static void main(String[] args) {
ClientConfiguration cfg = new ClientConfiguration().setAddresses("127.0.0.1:10800");
try (IgniteClient igniteClient = Ignition.startClient(cfg)) {
System.out.println(">>> Thin client put-get example started.");
final String CACHE_NAME = "put-get-example";
ClientCache<Integer, Address> cache = igniteClient.getOrCreateCache(CACHE_NAME);
System.out.format(">>> Created cache [%s].n", CACHE_NAME);
Integer key = 1;
Address val = new Address("1545 Jackson Street", 94612);
cache.put(key, val);
System.out.format(">>> Saved [%s] in the cache.n", val);
Address cachedVal = cache.get(key);
System.out.format(">>> Loaded [%s] from the cache.n", cachedVal);
}
catch (ClientException e) {
System.err.println(e.getMessage());
}
catch (Exception e) {
System.err.format("Unexpected failure: %sn", e);
}
}
Output:
>>> Thin client put-get example started.
>>> Created cache [put-get-example].
>>> Saved [Address [street=1545 Jackson Street, zip=94612]] in the cache.
>>> Loaded [Address [street=1545 Jackson Street, zip=94612]] from the cache.](https://image.slidesharecdn.com/apacheignite-ado-it-allkey-valuedb-200526104729/75/Apache-ignite-a-do-it-all-key-value-db-38-2048.jpg)
Uploaded byZaar Hai
Apache ignite - a do-it-all key-value db?
Apache Ignite is a versatile key/value database that supports both in-memory and on-disk storage, offering features like clustering, transactions, and ANSI SQL support. It presents various use cases and highlights its performance metrics, including CPU-bound storage costs and high insert/update/read rates. Despite its capabilities, the document notes challenges with tools and documentation, suggesting further development is needed for a smoother user experience.
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Apache ignite - a do-it-all key-value db?
- 1. Apache Ignite 🕯 Do-It-AllKey/Value DB?
- 2. hello! I am ZaarHai Cloud Architect at DoiT International linkedin.com/in/zaar 2
- 3.
- 4. A shortlist offeatures ✘ Key / Value DB ✘ Both in-memory and on-disk ✘ True (easy) clustering ✘ Transactions ✘ ANSI-99 SQL, with JDBC and ODBC support ✘ Grid computing 4
- 5.
- 6. Memory only 6 Distributed memorycache Node RAM Node RAM Node RAM
- 7. Memory only 7 Distributed memorycache Node RAM Node RAM Node RAM Redis-like performance
- 8. Memory + persistence 8 Distributedmemory cache Node RAM Node RAM Node RAM Node Disc Node Disc Node Disc
- 9. Memory + persistence 9 Distributedmemory cache Node RAM Node RAM Node RAM Persistent Redis done rightNode Disc Node Disc Node Disc
- 10. Memory + LOTSof persistence 10 Distributed memory cache Node RAM Node RAM Node RAM Node Disc Node Disc Node Disc
- 11. Memory + LOTSof persistence 11 Node RAM Node RAM Node RAM Couch DB, ES, Cassandra, etc. Node Disc Node Disc Node Disc Distributed memory cache
- 12.
- 13.
- 14. Cache data layout 14 Cache P3P2P1 ▩pri ▧ bak1 ▧ bak2
- 15. Cache data layout 15 Cache P3P2P1 ▩pri ▧ bak1 ▧ bak2 Node3 Node1 Node2 ✘ Replication factor ✘ Distributed store ✘ Zone awareness ✘ Consistency level
- 16. Inside a node 16 Syncmodes Fsync / buffer cache
- 17.
- 18.
- 19. No strong masters 19 🔑 key1 Pnum =hash(key, P_count) P3P2P1
- 20. No strong masters 20 🔑 key1 Pnum =hash(key, P_count) Consistent hashing E.g “modulo” P3P2P1
- 21. No strong masters 21 P3P2P1 ▩pri ▧ bak1 ▧ bak2 Node3 Node1 Node2 🔑 key1 P1 = hash(key1 , P_count) Node6 Node4 Node5
- 22. No strong masters 22 P3P2P1 ▩pri ▧ bak1 ▧ bak2 Node3 Node1 Node2 🔑 key1 P1 = hash(key1 , P_count) r_hash(P1 , N_count) = N4 , N2 , N3 , N6 , N5 , N1 Node6 Node4 Node5 Rendezvous hashing
- 23. No strong masters 23 P3P2P1 ▩pri ▧ bak1 ▧ bak2 Node3 Node1 Node2 🔑 key1 P1 = hash(key1 , P_count) Rendezvous hashing Node6 Node4 Node5 r_hash(P1 , N_count) = N4 , N2 , N3 , N6 , N5 , N1
- 24. Grid Computing 24 ✘ Writeyour own function ✘ Load them as Jars into Ignite nodes ✘ Run computations close to your data
- 25. Cheap Setup ThatMakes Sense 25 ✘ 2 x (2 CPU / 16 GB RAM) x 2 zones, 1 TB PD SSD per node ✘ Less than $250/TB ✘ 9 thin clients during random inserts, updates, and reads: ○ 3,682 inserts/s, 3.8ms median latency ○ 4,229 updates/s, 4.8ms median latency ○ 3,952 reads/s, 4.2ms median latency ✘ Completely CPU bound - you pay mainly for storage
- 26.
- 27. Running on K8s 27 ✘There are official docker images for Ignite Server ✘ There are guides for EKS, GKE, etc. ✘ Our improvements: https://github.com/doitintl/ignite-gke
- 28. No backups 28 ✘ Snapshottingcan work but quite tricky
- 29. Configuration 29 It’s all XMLwith Java Beans <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation=" http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd"> <bean class="org.apache.ignite.configuration.IgniteConfiguration"> <property name="dataStorageConfiguration"> <bean class="org.apache.ignite.configuration.DataStorageConfiguration"> <property name="defaultDataRegionConfiguration"> <bean class="org.apache.ignite.configuration.DataRegionConfiguration"> <property name="persistenceEnabled" value="true"/> </bean> </property> </bean> </property> ...
- 30. Tools 30 ✘ Couldn’t getCLI to work ✘ Web Console ○ Overcomplicated ○ Outdated docker images
- 31. Tools 31 ✘ Couldn’t getCLI to work ✘ Web Console ○ Overcomplicated ○ Outdated docker images Node3 Node1 Node2 Mongo
- 32. Tools 32 ✘ Couldn’t getCLI to work ✘ Web Console ○ Overcomplicated ○ Outdated docker images Node3 Node1 Node2 Mongo Web FE Web BE
- 33. Tools 33 ✘ Couldn’t getCLI to work ✘ Web Console ○ Overcomplicated ○ Outdated docker images Node3 Node1 Node2 Mongo Web FE Web BE agent REST
- 34. REST API ✘ Notreally REST - more like HTTP RPC GET /ignite?cmd=getorcreate&cacheName=foo 34
- 35. REST API ✘ Notreally REST - more like HTTP RPC GET /ignite?cmd=getorcreate&cacheName=foo ✘ Even better 😨 # Fetch cache partitions list GET /ignite?cmd=exe& name=org.apache.ignite.internal.visor.compute.VisorGatewayTask& p1=nid1;nid2;nid3& p2=org.apache.ignite.internal.visor.cache.VisorCachePartitionsTask& p3=org.apache.ignite.internal.visor.cache.VisorCachePartitionsTaskArg& p4=my_cache 35
- 36. Supported Client Protocols ✘HTTP “REST” API ✘ Memcached-compatible Binary Protocol ✘ Redis client protocol (partial support) ✘ Ignite Binary Client Protocol ○ “Ignite binary client protocol enables user applications to communicate with an existing Ignite cluster without starting a full-fledged Ignite node. An application can connect to the cluster through a raw TCP socket.” 36
- 37. Native Thin ClientLibraries ✘ Java ✘ .NET ✘ C++ ✘ Node.JS ✘ Python ✘ PHP 37
- 38. Thin Client Example(Java) 38 public static void main(String[] args) { ClientConfiguration cfg = new ClientConfiguration().setAddresses("127.0.0.1:10800"); try (IgniteClient igniteClient = Ignition.startClient(cfg)) { System.out.println(">>> Thin client put-get example started."); final String CACHE_NAME = "put-get-example"; ClientCache<Integer, Address> cache = igniteClient.getOrCreateCache(CACHE_NAME); System.out.format(">>> Created cache [%s].n", CACHE_NAME); Integer key = 1; Address val = new Address("1545 Jackson Street", 94612); cache.put(key, val); System.out.format(">>> Saved [%s] in the cache.n", val); Address cachedVal = cache.get(key); System.out.format(">>> Loaded [%s] from the cache.n", cachedVal); } catch (ClientException e) { System.err.println(e.getMessage()); } catch (Exception e) { System.err.format("Unexpected failure: %sn", e); } } Output: >>> Thin client put-get example started. >>> Created cache [put-get-example]. >>> Saved [Address [street=1545 Jackson Street, zip=94612]] in the cache. >>> Loaded [Address [street=1545 Jackson Street, zip=94612]] from the cache.
- 39. Final thoughts 39 ✓ Interestingtech ✓ Flexible performance ✓ Feature rich ✘ Tools are immature ✘ Docs need improvement ✘ For Java nuts?
- 40.