Russian version of this post is here

This summer I participated in the project Google Summer of Code in the Scala organization. My task was writing a library (stalagmite), which could generate by scala.meta effective and customizable replacement of conventional case classes with some convenient optimizations. Let’s analyze this description:

  • Effective, that is comparable speed of functions already provided in the `case class’, and more or less fast implementation of additional ones
  • Customizable – the ability to enable and disable various modes of code generation
  • Replacement – support for already available methods and functionality
  • Optimizations – reducing memory usage, boilerplate, increasing speed via macro generation

This list looks great, but making complete replacement for the powerful and elaborated case classes is difficult. There are many hacks special cases that are handled by the Scala compiler. The aim of the project was to support the necessary subset of such “cases” along with additional settings, the implementation of which without the use of macro-generation would require writing boilerplate code and bulky structures within the class. It was also required to provide an acceptable working time of macro-generated code, that I will check further by benchmarks.

Current status of the project

Currently the project provides 3 main operation mods together with a set of small improvements:

  • The duplication of the behavior of case class, in this mode all the basic functions is generated: toString, hashCode, copy, apply, unapply, Product and Serializable handled, generation of Generic LabelledGeneric, Typeable from Shapeless
  • Memoisation, interning, this technique disallows to duplicate objects that are equal by value, thus keeps in memory only one object and multiple references to it. It reduces the memory usage and time for comparing objects (object equality is equivalent to reference equality), but the creation of each object requires more resources. Stalagmite supports the interning of both the class and the fields inside it. There are also two sub-mods: weak and strong memoisation. The first mode stores the class objects within a pool using a weak reference, allowing the garbage collector to remove unused instances, the second mode keeps the normal link, and prohibits removing from memory objects, that get to the pool.
  • “Packaging” the class fields (in the project called heap optimization), this mode reduces the amount of memory for each instance of the class using the Boolean and Option[AnyVal] (for primitives) “packaging” in the bitmask and converting fields of type Option[T] to the type T that accepts null for the original case of None.

Generated classes with these mods also takes into account important property of immutability. Memoisation and fields packing implicitly use it.

Work on the library is not finished yet, many problems and ideas are documented in the repository Issues. Visit it if you are interested in the fate of the project.


Next there will be the long description of the benchmarks. For the impatient and those who want to learn only the results of performance testing there’s a TL;DR section at the end

Working time

First, let’s examine the time benchmarks, that used the JMH. Each of them represents the applying of some method to a collection of 5000 tuples or instances of a certain class. Number of such applyings in a second was counted for collection on average on each of the 15 threads. The results are given for processor Intel Core i5-4210U @ 1.70 GHz.

I will use the following notation for classes used in benchmarks:

  • caseClass – normal case class
  • caseClassMeta – macro-generated class that duplicates the functionality of caseClass
  • memoisedCaseClass – also an ordinary case class, created for comparison with memorization verison
  • memoisedMeta – the generated class with strong memoisation
  • memoisedWeak – the generated class with weak memoisation
  • optimizeHeapCaseClasscase class for comparison with heap optimization
  • optimizeHeapMeta – the generated class with heap optimization

TL;DR There are three groups of classes that were used during testing. caseClass.* tested duplication of case classes, memoised.* tested memorization, optimizeHeap* – fields packaging. *caseClass served as the baselines for each group, and were simple case classes.

The first group of benchmarks checks the speed of the main methods for case class: apply, copy, field access, hashCode, Product methods, serialization, toString,


Collection consisted of tuples with fields for creating classes. For each tuple the apply method from corresponding class was called.

Class Result
ApplyBenchmark.caseClass 19136.554 ± 637.993 ops/s
ApplyBenchmark.caseClassMeta 18331.622 ± 510.110 ops/s
ApplyBenchmark.memoisedCaseClass 20007.336 ± 276.490 ops/s
ApplyBenchmark.memoisedMeta 2560.862 ± 30.396 ops/s
ApplyBenchmark.memoisedMetaWeak 3714.472 ± 21.630 ops/s
ApplyBenchmark.optimizeHeapCaseClass 19397.820 ± 509.561 ops/s
ApplyBenchmark.optimizeHeapMeta 3949.541 ± 54.173 ops/s

memoised and optimizeHeap modes have the overhead of memoization and fields packaging, thus work slower than regular case class. caseClass shows the same speed as case class.


For each class instance in the collection 2 copies with different values in one of the two fields were created.

Class Result
CopyBenchmark.caseClass 5080.050 ± 284.304 ops/s
CopyBenchmark.caseClassMeta 5432.313 ± 336.334 ops/s
CopyBenchmark.memoisedCaseClass 4998.074 ± 173.456 ops/s
CopyBenchmark.memoisedMeta 853.461 ± 8.121 ops/s
CopyBenchmark.memoisedMetaWeak 1159.225 ± 63.359 ops/s
CopyBenchmark.optimizeHeapCaseClass 6899.622 ± 129.428 ops/s
CopyBenchmark.optimizeHeapMeta 959.581 ± 11.433 ops/s

The situation is similar to the previous benchmark. Method copy just creates a new object using apply.

Access to the fields

Each field of a class instance in the collection was read.

Class Result
FieldAccessBenchmark.caseClass 15390.926 ± 122.415 ops/s
FieldAccessBenchmark.caseClassMeta 15433.422 ± 254.224 ops/s
FieldAccessBenchmark.optimizeHeapCaseClass 22975.564 ± 803.286 ops/s
FieldAccessBenchmark.optimizeHeapMeta 4535.168 ± 50.179 ops/s

caseClass mode doesn’t differ from case class, field access method returns the actual field from the class. optimizeHeap mode performs the “unpacking” of the fields while reading, so working time is longer. memoised mode wasn’t included, because it’s equivalent to caseClass in this context.

.hashCode and .toString

For each class instance methods .hashCode'and .toString` were called.

Class Result
HashCodeBenchmark.caseClass 11307.343 ± 1278.621 ops/s
HashCodeBenchmark.caseClassMeta 12106.911 ± 263.225 ops/s
HashCodeBenchmark.memoisedCaseClass 16266.437 ± 292.010 ops/s
HashCodeBenchmark.memoisedMeta 24262.046 ± 1876.971 ops/s
HashCodeBenchmark.optimizeHeapCaseClass 4208.655 ± 323.614 ops/s
HashCodeBenchmark.optimizeHeapMeta 3078.390 ± 210.247 ops/s
Class Result
ToStringBenchmark.caseClass 1145.058 ± 34.190 ops/s
ToStringBenchmark.caseClassMeta 1296.309 ± 22.632 ops/s
ToStringBenchmark.memoisedCaseClass 1439.810 ± 106.486 ops/s
ToStringBenchmark.memoisedMeta 26745.501 ± 1026.738 ops/s
ToStringBenchmark.optimizeHeapCaseClass 548.055 ± 98.772 ops/s
ToStringBenchmark.optimizeHeapMeta 646.945 ± 29.493 ops/s

For caseClass and optimizeHeap modes the situation is similar as in the field access. Methods .hashCode and .toString get fields of the class for building hashes and strings, though still doing a lot of other work, so the difference is small. memoised mode works faster than case class baseline because of the special settings: memoisedHashCode and memoisedToString. They save the values of the two methods, and don’t count them many times.


This benchmark tested method productElement.

Class Result
ProductElementBenchmark.caseClass 13921.991 ± 631.164 ops/s
ProductElementBenchmark.caseClassMeta 13871.084 ± 1241.714 ops/s
ProductElementBenchmark.optimizeHeapCaseClass 21984.665 ± 636.940 ops/s
ProductElementBenchmark.optimizeHeapMeta 4305.256 ± 73.872 ops/s

Everything is the same as in benchmark on field access.


The entire collection of class instances was serialized and then deserialized.

Class Result
SerializationBenchmark.caseClass 190.118 ± 19.615 ops/s
SerializationBenchmark.caseClassMeta 205.555 ± 29.683 ops/s
SerializationBenchmark.memoisedCaseClass 250.941 ± 34.648 ops/s
SerializationBenchmark.memoisedMeta 316.477 ± 30.002 ops/s
SerializationBenchmark.memoisedMetaWeak 338.591 ± 39.945 ops/s
SerializationBenchmark.optimizeHeapCaseClass 93.594 ± 18.978 ops/s
SerializationBenchmark.optimizeHeapMeta 66.927 ± 8.449 ops/s

There are almost no differences from case class baselines. Complex operations of writing and reading serialized data outshine quick fields accessing and objects creating. Good conclusion – even difficult fields packing in optimizeHeap and memoization does not affect the objects serialization.


Each class instance was pattern-matched.

Class Result
UnapplyBenchmark.caseClass 14816.202 ± 939.746 ops/s
UnapplyBenchmark.caseClassMeta 13392.518 ± 431.781 ops/s
UnapplyBenchmark.optimizeHeapCaseClass 23635.361 ± 238.981 ops/s
UnapplyBenchmark.optimizeHeapMeta 4082.947 ± 61.865 ops/s

optimizeHeap mode shows slow results due to the fields unpacking, caseClass mode shows good performance regarding the case class.

The next group of benchmarks tested the generating mods features: methods to support Shapeless and speed of .equals with memoization.


shapeless mod generates objects of type Generic and LabelledGeneric that allow conversion of class instances to Hlist and back. Such conversions were performed for the entire collection.

Class Result
ShapelessBenchmark.caseClass 8406.639 ± 342.925 ops/s
ShapelessBenchmark.caseClassMeta 6653.700 ± 38.209 ops/s

Generated class works slower. Reasons aren’t clear, probably it’s because of Shapeless macro-generation.

.equals within Vector

This benchmark tested speed of .equals method in the case when data is placed in a Vector. A set of 1000000 random pairs of indices for comparisons was choosen. The indices were chosen with distance 10 or less and gradually increased, starting with 1. It allows to support data locality and data structure Vector is able to provide it.

Class Result
EqualsVectorBenchmark.caseClass 29.456 ± 1.851 ops/s
EqualsVectorBenchmark.caseClassMeta 30.252 ± 1.707 ops/s
EqualsVectorBenchmark.memoisedCaseClass 36.041 ± 3.207 ops/s
EqualsVectorBenchmark.memoisedMeta 36.401 ± 1.061 ops/s
EqualsVectorBenchmark.memoisedWeak 36.737 ± 2.372 ops/s
EqualsVectorBenchmark.optimizeHeapCaseClass 25.922 ± 1.398 ops/s
EqualsVectorBenchmark.optimizeHeapMeta 11.473 ± 0.828 ops/s

caseClass and memoised modes work as well as case class. In the first case there aren’t any differences in the implementations, in the second comparison by reference, not by value was used in the generated classes. It should work faster, but it doesn’t. Time to access an element in Vector overlaps all. In the case of optimizeHeap unpacking operation slows down .equals, even compared to the accessing time to the elements.

.equlas inner HashSet

HashSet uses .equals and .hashCode to build the hash table. The time for the entire collection of instances of class to be written into an empty HashSet was meausred.

Class Result
HashSetBenchmark.caseClass 3341.499 ± 44.082 ops/s
HashSetBenchmark.caseClassMeta 3665.179 ± 77.763 ops/s
HashSetBenchmark.memoisedCaseClass 3858.759 ± 49.616 ops/s
HashSetBenchmark.memoisedIntern 5009.943 ± 137.088 ops/s
HashSetBenchmark.memoisedMeta 6776.364 ± 39.766 ops/s
HashSetBenchmark.memoisedWeak 6401.936 ± 139.723 ops/s
HashSetBenchmark.optimizeHeapCaseClass 1566.175 ± 70.254 ops/s
HashSetBenchmark.optimizeHeapMeta 869.202 ± 22.643 ops/s

caseClass and optimizeHeap modes work normally. The first is similar to the baseline, the second is slower. But memoised mode here is way more interesting! The new class memoisedIntern is introduced here, it uses memoisedHashCode setting. It caches the hashCode and reduces the accessing time to the hash code. But even without it the generated class is faster than case class because of accelerated .equals method. With the caching of the hash code speed is greatly increased.


Benchmarks on memory usage worked simple. Two characteristics were measured: how much memory was used during the iteration and how much was used from the start of the run. The second characteristic is necessary to check how much memory, that cannot be removed by the garbage collector, is being producing. Several iterations was performed with output of memory usage dynamic.

There were 4 benchmarks:

  • memory usage measurment for case class duplication mode
  • measurement for packing fields
  • measurement for memoization when the data cannot be removed by the garbage collector
  • and when it can be

case class

Memory usage of 500 thousand case classes and generated classes was compared. Each of them consisted of the following fields: i: Int, b: Boolean, s: String.


Iteration Memory for iteration Memory after iteration
0 54659 kb 54571 kb
1 54574 kb 54499 kb
2 54877 kb 54689 kb
3 54801 kb 54570 kb
4 54691 kb 54595 kb

Generated class

Iteration Memory for iteration Memory after iteration
0 55032 kb 54934 kb
1 54740 kb 54626 kb
2 54896 kb 54835 kb
3 54687 kb 54612 kb
4 54920 kb 54845 kb

Absolutely identical.

Packaging fields

A similar comparison was performed: a class with fields packaging against case class. They contain the fields: i: Option[Int], s: Option[String], b1: Option[Boolean], b2: Option[Boolean], b3: Option[Boolean], b4: Option[Boolean]


Iteration Memory for iteration Memory after iteration
0 111830 kb 111732 kb
1 113230 kb 112418 kb
2 112626 kb 112325 kb
3 112698 kb 112315 kb
4 113117 kb 112715 kb

The package fields

Iteration Memory for iteration Memory after iteration
0 40399 kb 40281 kb
1 42455 kb 39790 kb
2 42949 kb 40088 kb
3 42539 kb 39811 kb
4 43195 kb 40395 kb

Fileds packaging reduces memory usage by almost 3 times. This benchmark clearly shows how redundant are Option and Boolean types.

Memoization without deletable data

Let’s finally proceed to the most interesting and demonstrative memory benchmarks. In the first created instances of classes could not be removed by the garbage collector. There were 4 classes: case class, with strong memoization, with strong memoization and inner fields interning, and weak memoization. They contain the following fields: b: Boolean, s: String and the string fields s was interned in the 3rd case. Two types of data were used to create instances of these classes: data with a large number of repetitions and data with various elements.

Data with repetitions

500 thousand elements, the 2-length strings of digits and letters. The size of all different combinations of such strings and Boolean is much smaller than the size of the collection, so there were many duplicates.


Iteration Memory for iteration Memory after iteration
0 45174 kb 50249 kb
1 46968 kb 50343 kb
2 47010 kb 50478 kb
3 46936 kb 50540 kb
4 46980 kb 50646 kb


Iteration Memory for iteration Memory after iteration
0 11831 kb 11537 kb
1 11781 kb 11570 kb
2 11770 kb 11601 kb
3 11729 kb 11601 kb
4 11729 kb 11601 kb

memoisedMeta with string interning

Iteration Memory for iteration Memory after iteration
0 11733 kb 11732 kb
1 11718 kb 11732 kb
2 11729 kb 11742 kb
3 11718 kb 11742 kb
4 11728 kb 11753 kb


Iteration Memory for iteration Memory after iteration
0 11666 kb 11659 kb
1 11684 kb 11680 kb
2 11663 kb 11680 kb
3 11662 kb 11680 kb
4 11683 kb 11700 kb

Memoization is doing its job. All repetitions in data are added to the cache and not duplicated as in the case of case class.

Data without repetition

Also 500 thousand elements, but the strings have length 5. The data hasn’t repetitions now.


Iteration Memory for iteration Memory after iteration
0 54937 kb 54938 kb
1 53696 kb 53724 kb
2 54271 kb 53308 kb
3 54590 kb 53211 kb
4 54667 kb 53191 kb


Iteration Memory for iteration Memory after iteration
0 78619 kb 77984 kb
1 74883 kb 140421 kb
2 82160 kb 210863 kb
3 75171 kb 273346 kb
4 74882 kb 336093 kb

memoisedMeta with string interning

Iteration Memory for iteration Memory after iteration
0 95091 kb 94141 kb
1 86867 kb 168425 kb
2 102367 kb 259074 kb
3 86810 kb 333071 kb
4 86753 kb 407689 kb


Iteration Memory for iteration Memory after iteration
0 105562 kb 105562 kb
1 66527 kb 105683 kb
2 66392 kb 105669 kb
3 66423 kb 105686 kb
4 66406 kb 105686 kb

Here case class’s winning. Strong memoization is continuously writing new data to the cache, consuming a large amount of memory. String interning only worsens the situation. Weak memoization uses the memory better, but it has overhead in cache building.

Memoization with the garbage collector

In this benchmark after creating 500 thousand instances only 20000 references to them were left. This allowed the garbage collector to do its dirty work.

Data with repetitions


Iteration Memory for iteration Memory after iteration
0 1914 kb 1923 kb
1 1871 kb 1920 kb
2 1852 kb 1897 kb
3 1856 kb 1879 kb
4 1859 kb 1863 kb


Iteration Memory for iteration Memory after iteration
0 1662 kb 1663 kb
1 445 kb 1376 kb
2 459 kb 1367 kb
3 448 kb 1347 kb
4 468 kb 1347 kb

memoisedMeta with string interning

Iteration Memory for iteration Memory after iteration
0 1347 kb 1348 kb
1 473 kb 1351 kb
2 458 kb 1341 kb
3 458 kb 1331 kb
4 468 kb 1331 kb


Iteration Memory for iteration Memory after iteration
0 1583 kb 1583 kb
1 900 kb 1521 kb
2 886 kb 1496 kb
3 906 kb 1494 kb
4 909 kb 1498 kb

All the different combinations of data aggin can be fully written to the cache. Strong memoization gives a good improvement over the case class, weak a little worse.

Data without repetition


Iteration Memory for iteration Memory after iteration
0 2299 kb 2299 kb
1 2228 kb 2341 kb
2 2187 kb 2341 kb
3 2207 kb 2341 kb
4 2187 kb 2341 kb


Iteration Memory for iteration Memory after iteration
0 66468 kb 66468 kb
1 66920 kb 132920 kb
2 62015 kb 193819 kb
3 70686 kb 264037 kb
4 62875 kb 326444 kb

memoisedMeta with string interning

Iteration Memory for iteration Memory after iteration
0 82753 kb 82754 kb
1 81617 kb 163902 kb
2 75799 kb 239233 kb
3 91769 kb 330278 kb
4 75296 kb 403948 kb


Iteration Memory for iteration Memory after iteration
0 41861 kb 41861 kb
1 3089 kb 42294 kb
2 2744 kb 42382 kb
3 2676 kb 42402 kb
4 2676 kb 42423 kb

Here everything is much worse. Strong memoization stores all the data in the cache, nothing is deleted, and the cache is becoming huge. Weak memoisation stores only what is needed for iteration in the cache, but it still takes a lot of memory. This is the worst case to use memoization. It could easily lead to OutOfMemoryError.


The speed and memory consumption measurements show that there’s no such situation when the generated class is definitely better than the case class. There’s either the gain in speed, but with more memory usage, or vice versa. Generated classes, if no additional generation modes applied, duplicate the effectiveness of the case class. Field packaging consumes less memory, but behaves slowly in accessing to class fields and .apply method. Memoization speeds up the .equals method, but also has overhead in .apply. Memory usage depends on the context. With a large number of duplicates memoization caches them and stores only references. When data has almost no duplicates, especially in the case when data is being cleaned by the garbage collector, the memory consumption increases significantly.

In the end

The conclusion will be simple. Built-in case classes work very well in most cases. Stalagmite provides an alternative to them, exchanging memory for speed and vice versa. There are some ideas on how to reduce the boilerplate and add new optimizations in developing plans, but complete replacement the case class is impossible. So use this library wisely. I hope I showed you the strengths and weaknesses of the current implementation :)