单节点Map场景下Hazelcast较Ignite慢3-4倍是否符合预期？

Great question—let’s unpack why you might be seeing this big gap and whether it’s expected, or if there’s something off with your test setup.

First: Yes, Default Config Differences Can Drive This Gap #

It’s entirely possible to see significant throughput differences between the two out of the box, even on a single node, because their default configurations prioritize different things:

• Serialization Efficiency: This is often the biggest culprit. Ignite defaults to its BinaryMarshaller, a highly optimized binary serialization format that avoids reflection overhead for most cases. Hazelcast, by contrast, defaults to IdentifiedDataSerializable—but if your Customer class doesn’t implement this interface, it’ll fall back to slower Java serialization. That alone can easily account for a 2-3x difference in put/get throughput.
• Concurrency & Locking: Hazelcast’s IMap uses partition-level locking by default, even in single-node mode. Ignite’s cache, on the other hand, uses more fine-grained concurrency controls (like entry-level locking for some operations) that can scale better under high throughput workloads.
• Cluster Overhead Overrides: Ignite automatically disables some cluster-specific overhead (like replication checks) when running in single-node mode, while Hazelcast retains more of its distributed infrastructure even for a single instance—adding small but cumulative overhead to each operation.

But Let’s Rule Out Test Setup Issues First #

Before accepting this gap as expected, double-check these common JMH testing pitfalls:

• Insufficient Warmup: JMH needs time to let the JIT compiler optimize your code. Make sure you’re using at least 5 warmup iterations (e.g., @Warmup(iterations = 5, time = 1)). Without proper warmup, you might be measuring unoptimized code execution which can skew results dramatically.
• Incorrect State Management: Are you initializing the Hazelcast/Ignite instances in a @Setup(Level.Trial) method? If you’re creating the instance inside the benchmark method, you’re measuring instance startup time alongside put/get operations—this would definitely skew Hazelcast’s results more than Ignite’s.
• Uniform Data Size & Distribution: Are you using the same Customer object size and key distribution for both tests? If your test data varies between the two benchmarks, that could create artificial differences.
• Backup Configuration: While single-node defaults usually set backup count to 0, double-check: Hazelcast’s Config might have implicit backup settings, while Ignite might disable backups entirely in single-node mode. Run mapConfig.getBackupCount() (Hazelcast) or cacheConfiguration.getBackups() (Ignite) to confirm.

How to Validate & Narrow the Gap #

If you want to make a more apples-to-apples comparison:

1. Align Serialization:

   • For Hazelcast, configure a custom serializer for your Customer class (or implement IdentifiedDataSerializable):

     SerializerConfig serializerConfig = new SerializerConfig()
         .setTypeClass(Customer.class)
         .setImplementation(new CustomerSerializer());
     config.getSerializationConfig().addSerializerConfig(serializerConfig);
     

   • For Ignite, ensure you’re not relying on any implicit serialization optimizations that Hazelcast doesn’t have (e.g., avoid Ignite’s @QuerySqlField annotations if you’re not using SQL in your test).

2. Tune for Single Node:

   • For Hazelcast, disable any distributed features you don’t need:

     mapConfig.setBackupCount(0);
     mapConfig.setAsyncBackupCount(0);
     

   • For Ignite, make sure you’re using a local cache configuration explicitly:

     CacheConfiguration<Long, Customer> cacheConfig = new CacheConfiguration<>();
     cacheConfig.setCacheMode(CacheMode.LOCAL);
     

3. Check Official Benchmarks: Both projects publish their own benchmark results. While most focus on cluster scenarios, you’ll find that single-node throughput differences can be significant when using default settings.

Final Takeaway #

A 3-4x throughput gap in default single-node configurations is plausible, especially if serialization is unoptimized on the Hazelcast side. But don’t skip validating your JMH setup—small mistakes there can create misleading results. Once you align key configurations like serialization and single-node optimizations, the gap should narrow, though Ignite might still hold a slight edge in raw throughput for simple put/get operations.

内容的提问来源于stack exchange，提问作者Prasanth Ravi