README.md

sturdyc: a caching library for building sturdy systems

Sturdyc is an in-memory cache that supports non-blocking reads and has a configurable number of shards that makes it possible to achieve writes without any lock contention. The xxhash algorithm is used for efficient key distribution.

It has all the functionality you would expect from a caching library, but what sets it apart are the features designed to make I/O heavy applications both robust and highly performant.

Installing

go get github.com/creativecreature/sturdyc

At a glance

Deduplication

sturdyc performs in-flight tracking for every key. This also works for batch operations, where it can deduplicate a batch of cache misses and then assemble the response by picking records from multiple in-flight requests.

Early refreshes

There is also a lot of extra functionality you can enable, one being early refreshes which instructs the cache to refresh the keys which are in active rotation, thereby preventing them from ever expiring. This can have a huge impact on an applications latency as you're able to continiously serve the most frequently used data from memory:

sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, exponentialBackOff)

Batching

When the cache retrieves data from a batchable source, it will disassemble the response and then cache each record individually based on the permutations of the options with which it was fetched.

This can be used to significantly reduce the application's outgoing requests by also enabling refresh coalescing. Internally, sturdyc creates a buffer for each unique option set and gathers IDs until the idealBatchSize is reached or the batchBufferTimeout expires:

sturdyc.WithRefreshCoalescing(idealBatchSize, batchBufferTimeout)

Distributed key-value store

You can also configure sturdyc to synchronize its in-memory cache with a distributed key-value store of your choosing:

sturdyc.WithDistributedStorage(storage),

Evictions

The cache runs a background job which continuously evicts expired records from each shard. However, there are options to both tweak the interval and disable the functionality altogether. This is can give you a slight performance boost in situations where you're unlikely to exceed any memory limits.

When the cache reaches its capacity, a fallback eviction is triggered. This process performs evictions on a per-shard basis, selecting records for removal based on recency. The eviction algorithm uses quickselect, which has an O(N) time complexity without requiring write locks on reads to update a recency list.

Latency improvements

Below is a screenshot showing the latency improvements we've observed after replacing our old cache with this package:

   

In addition to this, we've seen our number of outgoing requests decrease by more than 90% after enabling refresh coalescing.

Adding sturdyc to your application:

I have tried to design the API in a way that should make it effortless to add sturdyc to an existing application. We'll use the following two methods of an API client as examples:

// Order retrieves a single order by ID.
func (c *Client) Order(ctx context.Context, id string) (Order, error) {
	timeoutCtx, cancel := context.WithTimeout(ctx, c.timeout)
	defer cancel()

	var response Order
	err := requests.URL(c.orderURL).
		Pathf("/order/%s", id).
		ToJSON(&response).
		Fetch(timeoutCtx)

	return response, err
}

// Orders retrieves a batch of orders by their IDs.
func (c *Client) Orders(ctx context.Context, ids []string) (map[string]Order, error) {
	timeoutCtx, cancel := context.WithTimeout(ctx, c.timeout)
	defer cancel()

	var response map[string]Order
	err := requests.URL(c.orderURL).
		Path("/orders").
		Param("ids", strings.Join(ids, ",")).
		ToJSON(&response).
		Fetch(timeoutCtx)

	return response, err
}

All we have to do is wrap the code that retrieves the data in a function, and then hand it over to our cache client:

func (c *Client) Order(ctx context.Context, id string) (Order, error) {
	fetchFunc := func(ctx context.Context) (Order, error) {
		timeoutCtx, cancel := context.WithTimeout(ctx, c.timeout)
		defer cancel()

		var response Order
		err := requests.URL(c.orderURL).
			Pathf("/order/%s", id).
			ToJSON(&response).
			Fetch(timeoutCtx)

		return response, err
	}

	return c.cache.GetOrFetch(ctx, "order-"+id, fetchFunc)
}

func (c *Client) Orders(ctx context.Context, ids []string) (map[string]Order, error) {
	fetchFunc := func(ctx context.Context, cacheMisses []string) (map[string]Order, error) {
		timeoutCtx, cancel := context.WithTimeout(ctx, c.timeout)
		defer cancel()

		var response map[string]Order
		err := requests.URL(c.orderURL).
			Path("/orders").
			Param("ids", strings.Join(cacheMisses, ",")).
			ToJSON(&response).
			Fetch(timeoutCtx)

		return response, err
	}

	return c.cache.GetOrFetchBatch(ctx, ids, c.persistentCache.BatchKeyFn("orders"), fetchFunc)
}

The example above retrieves the data from an HTTP API, but it's just as easy to wrap a database query, a remote procedure call, a disk read, or any other I/O operation. We can also use closures to make sure that the function that retrieves the data has all of the values it needs.

Next, we'll look at how to configure the cache in more detail.

Table of contents

I've included examples that cover the entire API, and I encourage you to read these examples in the order they appear. Most of them build on each other, and many share configurations. Here is a brief overview of what the examples are going to cover:

• creating a cache client
• stampede protection
• early refreshes
• caching non-existent records
• caching batch endpoints per record
• cache key permutations
• refresh coalescing
• request passthrough
• distributed storage
• custom metrics
• generics

Creating a cache client

The first thing you will have to do is to create a cache client to hold your configuration:

	// Maximum number of entries in the cache. Exceeding this number will trigger
	// an eviction (as long as the "evictionPercentage" is greater than 0).
	capacity := 10000
	// Number of shards to use. Increasing this number will reduce write lock collisions.
	numShards := 10
	// Time-to-live for cache entries.
	ttl := 2 * time.Hour
	// Percentage of entries to evict when the cache reaches its capacity. Setting this
	// to 0 will make writes a no-op until an item has either expired or been deleted.
	evictionPercentage := 10

	// Create a cache client with the specified configuration.
	cacheClient := sturdyc.New[int](capacity, numShards, ttl, evictionPercentage)

	cacheClient.Set("key1", 99)
	log.Println(cacheClient.Size())
	log.Println(cacheClient.Get("key1"))

	cacheClient.Delete("key1")
	log.Println(cacheClient.Size())
	log.Println(cacheClient.Get("key1"))

Next, we'll look at some of the more advanced features.

Stampede protection

Cache stampedes (also known as thundering herd) occur when many requests for a particular piece of data, which has just expired or been evicted from the cache, come in at once.

Preventing this has been one of the key objectives for this package. We do not want to cause a significant load on an underlying data source every time one of our keys expires.

The GetOrFetch function takes a key and a function for retrieving the data if it's not in the cache. The cache is going to ensure that we never have more than a single request per key. It achieves this by tracking all of the in-flight requests:

	var count atomic.Int32
	fetchFn := func(_ context.Context) (int, error) {
		count.Add(1)
		time.Sleep(time.Second)
		return 1337, nil
	}

	var wg sync.WaitGroup
	for i := 0; i < 5; i++ {
		wg.Add(1)
		go func() {
			// We can ignore the error given the fetchFn we're using.
			val, _ := cacheClient.GetOrFetch(context.Background(), "key2", fetchFn)
			log.Printf("got value: %d\n", val)
			wg.Done()
		}()
	}
	wg.Wait()

	log.Printf("fetchFn was called %d time\n", count.Load())
	log.Println(cacheClient.Get("key2"))

Running this program we'll see that our requests for "key2" were deduplicated, and that the fetchFn only got called once:

❯ go run .
2024/05/21 08:06:29 got value: 1337
2024/05/21 08:06:29 got value: 1337
2024/05/21 08:06:29 got value: 1337
2024/05/21 08:06:29 got value: 1337
2024/05/21 08:06:29 got value: 1337
2024/05/21 08:06:29 fetchFn was called 1 time
2024/05/21 08:06:29 1337 true

For data sources that supports batching, we're able to use the GetOrFetchBatch function. To demonstrate this, I'll create a mock function that sleeps for 5 seconds, and then returns a map with a numerical value for every ID:

	var count atomic.Int32
	fetchFn := func(_ context.Context, ids []string) (map[string]int, error) {
		count.Add(1)
		time.Sleep(time.Second * 5)

		response := make(map[string]int, len(ids))
		for _, id := range ids {
			num, _ := strconv.Atoi(id)
			response[id] = num
		}

		return response, nil
	}

Next, we'll need some batches to test with, so I created three batches with 5 IDs each:

	batches := [][]string{
		{"1", "2", "3", "4", "5"},
		{"6", "7", "8", "9", "10"},
		{"11", "12", "13", "14", "15"},
	}

IDs can often be fetched from multiple data sources. Hence, we'll want to prefix the ID in order to make the cache key unique. The package provides more functionality for this that we'll see later on, but for now we'll use the most simple version which adds a string prefix to every ID:

	keyPrefixFn := cacheClient.BatchKeyFn("my-data-source")

We can now request each batch in a separate goroutine:

	for _, batch := range batches {
		go func() {
			res, _ := cacheClient.GetOrFetchBatch(context.Background(), batch, keyPrefixFn, fetchFn)
			log.Printf("got batch: %v\n", res)
		}()
	}

	// Give the goroutines above a chance to run to ensure that the batches are in-flight.
	time.Sleep(time.Second * 3)

At this point, the cache should have in-flight requests for IDs 1-15. Knowing this, we'll test the stampede protection by launching another five goroutines. Each goroutine is going to request two random IDs from our batches:

	// Launch another 5 goroutines that are going to pick two random IDs from any of the batches.
	var wg sync.WaitGroup
	for i := 0; i < 5; i++ {
		wg.Add(1)
		go func() {
			ids := []string{batches[rand.IntN(2)][rand.IntN(4)], batches[rand.IntN(2)][rand.IntN(4)]}
			res, _ := cacheClient.GetOrFetchBatch(context.Background(), ids, keyPrefixFn, fetchFn)
			log.Printf("got batch: %v\n", res)
			wg.Done()
		}()
	}

	wg.Wait()
	log.Printf("fetchFn was called %d times\n", count.Load())

Running this program, and looking at the logs, we'll see that the cache is able to pick IDs from different batches:

❯ go run .
2024/05/21 09:14:23 got batch: map[8:8 9:9]
2024/05/21 09:14:23 got batch: map[4:4 9:9] <---- NOTE: ID 4 and 9 are part of different batches
2024/05/21 09:14:23 got batch: map[11:11 12:12 13:13 14:14 15:15]
2024/05/21 09:14:23 got batch: map[1:1 7:7] <---- NOTE: ID 1 and 7 are part of different batches
2024/05/21 09:14:23 got batch: map[10:10 6:6 7:7 8:8 9:9]
2024/05/21 09:14:23 got batch: map[3:3 9:9] <---- NOTE: ID 3 and 9 are part of different batches
2024/05/21 09:14:23 got batch: map[1:1 2:2 3:3 4:4 5:5]
2024/05/21 09:14:23 got batch: map[4:4 9:9] <---- NOTE: ID 4 and 9 are part of different batches
2024/05/21 09:14:23 fetchFn was called 3 times <---- NOTE: We only generated 3 outgoing requests.

And on the last line, we can see that the additional calls didn't generate any further outgoing requests. The entire example is available here.

Early refreshes

Being able to prevent your most frequently used records from ever expiring can have a significant impact on your application's latency. Therefore, the package provides a WithEarlyRefreshes option, which instructs the cache to continuously refresh these records in the background before they expire.

A refresh gets scheduled if a key is requested again after a configurable amount of time has passed. This is an important distinction because it means that the cache doesn't just naively refresh every key it's ever seen. Instead, it only refreshes the records that are actually in active rotation, while allowing unused keys to be deleted once their TTL expires.

Below is an example configuration that you can use to enable this functionality:

func main() {
	// Set a minimum and maximum refresh delay for the record. This is
	// used to spread out the refreshes of our entries evenly over time.
	// We don't want our outgoing requests graph to look like a comb that
    // sends a spike of refreshes every 30 ms.
	minRefreshDelay := time.Millisecond * 10
	maxRefreshDelay := time.Millisecond * 30
	// The base used for exponential backoff when retrying a refresh. Most of the
	// time, we perform refreshes well in advance of the records expiry time.
	// Hence, we can use this to make it easier for a system that is having
	// trouble to get back on it's feet by making fewer refreshes when we're
	// seeing a lot of errors. Once we receive a successful response, the
	// refreshes return to their original frequency. You can set this to 0
	// if you don't want this behavior.
	retryBaseDelay := time.Millisecond * 10

	// Create a cache client with the specified configuration.
	cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
		sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, retryBaseDelay),
	)
}

And to get a feeling for how this works, we'll use the configuration above and then create a simple API client which embedds the cache:

type API struct {
	*sturdyc.Client[string]
}

func NewAPI(c *sturdyc.Client[string]) *API {
	return &API{c}
}

func (a *API) Get(ctx context.Context, key string) (string, error) {
	// This could be an API call, a database query, etc.
    fetchFn := func(_ context.Context) (string, error) {
		log.Printf("Fetching value for key: %s\n", key)
		return "value", nil
	}
	return a.GetOrFetch(ctx, key, fetchFn)
}

Now we can return to our main function to create an instance of it, and then call the Get method in a loop:

func main() {
	// ...

	cacheClient := sturdyc.New[string](...)

	// Create a new API instance with the cache client.
	api := NewAPI(cacheClient)

	// We are going to retrieve the values every 10 milliseconds, however the
	// logs will reveal that actual refreshes fluctuate randomly within a 10-30
	// millisecond range.
	for i := 0; i < 100; i++ {
		val, err := api.Get(context.Background(), "key")
		if err != nil {
			log.Println("Failed to  retrieve the record from the cache.")
			continue
		}
		log.Printf("Value: %s\n", val)
		time.Sleep(minRefreshDelay)
	}
}

Running this program, we're going to see that the value gets refreshed once every 2-3 retrievals:

go run .
2024/04/07 09:05:29 Fetching value for key: key
2024/04/07 09:05:29 Value: value
2024/04/07 09:05:29 Value: value
2024/04/07 09:05:29 Fetching value for key: key
2024/04/07 09:05:29 Value: value
2024/04/07 09:05:29 Value: value
2024/04/07 09:05:29 Value: value
2024/04/07 09:05:29 Fetching value for key: key
...

This is going to reduce your response times significantly because none of your users will have to wait for the I/O operation that refreshes the data. It's always performed in the background as long as the key is being continuously requested. Being afraid that the record might get too stale if users stop requesting it is an indication of a TTL that is set too high. Remember, even if the TTL is exceeded and the key expires, you'll still get deduplication if it's suddenly requested in a burst again. The only difference is that the users will have to wait for the I/O operation that retrieves it.

Additionally, to provide a degraded experience when an upstream system encounters issues, you can set a high TTL and a low refresh time. When everything is working as expected, the records will be refreshed continuously. However, if the upstream system encounters issues and stops responding, you can fall back to cached records for the duration of the TTL.

What if the record was deleted? Our cache might use a 2-hour-long TTL, and we definitely don't want it to take that long for the deletion to propagate.

However, if we were to modify our client so that it returns an error after the first request:

type API struct {
	count int
	*sturdyc.Client[string]
}

func NewAPI(c *sturdyc.Client[string]) *API {
	return &API{0, c}
}

func (a *API) Get(ctx context.Context, key string) (string, error) {
	fetchFn := func(_ context.Context) (string, error) {
		a.count++
		log.Printf("Fetching value for key: %s\n", key)
		if a.count == 1 {
			return "value", nil
		}
		return "", errors.New("error this key does not exist")
	}
	return a.GetOrFetch(ctx, key, fetchFn)
}

and then run the program again:

cd examples/stampede
go run .

We'll see that the exponential backoff kicks in, resulting in more iterations for every refresh, but the value is still being printed:

2024/05/09 13:22:03 Fetching value for key: key
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:03 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Value: value
2024/05/09 13:22:04 Fetching value for key: key

This is a bit tricky because how you determine if a record has been deleted is going to vary based on your data source. It could be a status code, zero value, empty list, specific error message, etc. There is no way for the cache to figure this out implicitly.

It couldn't simply delete a record every time it receives an error. If an upstream system goes down, we want to be able to serve stale data for the duration of the TTL, while reducing the frequency of our refreshes to make it easier for them to recover.

Therefore, if a record is deleted, we'll have to explicitly inform the cache about it by returning a custom error:

fetchFn := func(_ context.Context) (string, error) {
		a.count++
		log.Printf("Fetching value for key: %s\n", key)
		if a.count == 1 {
			return "value", nil
		}
		return "", sturdyc.ErrNotFound
	}

This tell's the cache that the record is no longer available at the underlying data source. Therefore, if this record is being fetched as a background refresh, the cache will quickly see if it has a record for this key, and subsequently delete it.

If we run this application again we'll see that it works, and that we're no longer getting any cache hits. This leads to outgoing requests for every iteration:

2024/05/09 13:40:47 Fetching value for key: key
2024/05/09 13:40:47 Value: value
2024/05/09 13:40:47 Value: value
2024/05/09 13:40:47 Value: value
2024/05/09 13:40:47 Fetching value for key: key
2024/05/09 13:40:47 Failed to  retrieve the record from the cache.
2024/05/09 13:40:47 Fetching value for key: key
2024/05/09 13:40:47 Failed to  retrieve the record from the cache.
2024/05/09 13:40:47 Fetching value for key: key
2024/05/09 13:40:47 Failed to  retrieve the record from the cache.
2024/05/09 13:40:47 Fetching value for key: key
2024/05/09 13:40:47 Failed to  retrieve the record from the cache.

Please note that we only have to return the sturdyc.ErrNotFound when we're using GetOrFetch. For GetOrFetchBatch, we'll simply omit the key from the map we're returning. I think this inconsistency is a little unfortunate, but it was the best API I could come up with. Having to return an error like this if just a single ID wasn't found:

	batchFetchFn := func(_ context.Context, cacheMisses []string) (map[string]string, error) {
		response, err := myDataSource(cacheMisses)
		for _, id := range cacheMisses {
			// NOTE: Don't do this, it's just an example.
			if response[id]; !id {
                return response, sturdyc.ErrNotFound
            }
		}
		return response, nil
	}

and then have the cache swallow that error and return nil, felt much less intuitive.

The entire example is available here.

Non-existent records

In the example above, we could see that once we delete the key, the following iterations lead to a continuous stream of outgoing requests. This will happen for every ID that doesn't exist at the data source. If we can't retrieve it, we can't cache it. If we can't cache it, we can't serve it from memory. If this happens frequently, we'll experience a lot of I/O operations, which will significantly increase our system's latency.

The reasons why someone might request IDs that don't exist can vary. It could be due to a faulty CMS configuration, or perhaps it's caused by a slow ingestion process where it takes time for a new entity to propagate through a distributed system. Regardless, this will negatively impact our systems performance.

To address this issue, we can instruct the cache to mark these IDs as missing records. Missing records are refreshed at the same frequency as regular records. Hence, if an ID is continuously requested, and the upstream eventually returns a valid response, we'll see it propagate to our cache.

To illustrate, I'll make some small modifications to the code from the previous example. The only thing I'm going to change is to make the API client return a ErrNotFound for the first three requests:

type API struct {
	*sturdyc.Client[string]
	count int
}

func NewAPI(c *sturdyc.Client[string]) *API {
	return &API{c, 0}
}

func (a *API) Get(ctx context.Context, key string) (string, error) {
	fetchFn := func(_ context.Context) (string, error) {
		a.count++
		log.Printf("Fetching value for key: %s\n", key)
		if a.count > 3 {
			return "value", nil
		}
		// This error tells the cache that the data does not exist at the source.
		return "", sturdyc.ErrNotFound
	}
	return a.GetOrFetch(ctx, key, fetchFn)
}

Next, we'll just have to enable missing record storage which tells the cache that anytime it gets a ErrNotFound error it should mark the key as missing:

func main() {
	// ...

	cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
		sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, retryBaseDelay),
		sturdyc.WithMissingRecordStorage(),
	)

	api := NewAPI(cacheClient)

	// ...
	for i := 0; i < 100; i++ {
		val, err := api.Get(context.Background(), "key")
		// The cache returns ErrMissingRecord for any key that has been marked as missing.
		// You can use this to exit-early, or return some type of default state.
		if errors.Is(err, sturdyc.ErrMissingRecord) {
			log.Println("Record does not exist.")
		}
		if err == nil {
			log.Printf("Value: %s\n", val)
		}
		time.Sleep(minRefreshDelay)
	}
}

Running this program, we'll see that the record is missing during the first 3 refreshes, and then transitions into having a value:

❯ go run .
2024/05/09 21:25:28 Fetching value for key: key
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Fetching value for key: key
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Fetching value for key: key
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Record does not exist.
2024/05/09 21:25:28 Fetching value for key: key
2024/05/09 21:25:28 Value: value
2024/05/09 21:25:28 Value: value
2024/05/09 21:25:28 Value: value
2024/05/09 21:25:28 Fetching value for key: key
...

Please note that this functionality is implicit for GetOrFetchBatch. You simply just have to omit the key from the map:

	batchFetchFn := func(_ context.Context, cacheMisses []string) (map[string]string, error) {
		// The cache will check if every ID in cacheMisses is present in the response.
		// If it finds any IDs that are missing it will proceed to mark them as missing
		// if missing record storage is enabled.
		response, err := myDataSource(cacheMisses)
		return response, nil
	}

The entire example is available here.

Batch endpoints

One challenge with caching batchable endpoints is that you have to find a way to reduce the number of keys. To illustrate, let's say that we have 10 000 records, and an endpoint for fetching them that allows for batches of 20. The IDs for the batch are supplied as query parameters, for example, https://example.com?ids=1,2,3,4,5,...20. If we were to use this as the cache key, the way many CDNs would do, we could quickly calculate the number of keys we would generate like this:

$$ C(n, k) = \binom{n}{k} = \frac{n!}{k!(n-k)!} $$

For $n = 10,000$ and $k = 20$, this becomes:

$$ C(10,000, 20) = \binom{10,000}{20} = \frac{10,000!}{20!(10,000-20)!} $$

This results in an approximate value of:

$$ \approx 4.032 \times 10^{61} $$

and this is if we're sending perfect batches of 20. If we were to do 1 to 20 IDs (not just exactly 20 each time) the total number of combinations would be the sum of combinations for each k from 1 to 20.

At this point, we would essentially just be paying for extra RAM, as the hit rate for each key would be so low that we'd have better odds of winning the lottery.

To prevent this, sturdyc pulls the response apart and caches each record individually. This effectively prevents super-polynomial growth in the number of cache keys because the batch itself is never going to be inlcuded in the key.

To get a feeling for how this works, let's once again build a small example application. This time, we'll start with the API client:

type API struct {
	*sturdyc.Client[string]
}

func NewAPI(c *sturdyc.Client[string]) *API {
	return &API{c}
}

func (a *API) GetBatch(ctx context.Context, ids []string) (map[string]string, error) {
	// We are going to use a cache a key function that prefixes each id.
	// This makes it possible to save the same id for different data sources.
	cacheKeyFn := a.BatchKeyFn("some-prefix")

	// The fetchFn is only going to retrieve the IDs that are not in the cache.
	fetchFn := func(_ context.Context, cacheMisses []string) (map[string]string, error) {
		log.Printf("Cache miss. Fetching ids: %s\n", strings.Join(cacheMisses, ", "))
		// Batch functions should return a map where the key is the id of the record.
		response := make(map[string]string, len(cacheMisses))
		for _, id := range cacheMisses {
			response[id] = "value"
		}
		return response, nil
	}

	return a.GetOrFetchBatch(ctx, ids, cacheKeyFn, fetchFn)
}

and we're going to use the same cache configuration as the previous example, so I've omitted it for brevity:

func main() {
	// ...

	// Create a new API instance with the cache client.
	api := NewAPI(cacheClient)

	// Make an initial call to make sure that IDs 1-10 are retrieved and cached.
	log.Println("Seeding ids 1-10")
	ids := []string{"1", "2", "3", "4", "5", "6", "7", "8", "9", "10"}
	api.GetBatch(context.Background(), ids)
	log.Println("Seed completed")

	// To demonstrate that the records have been cached individually, we can continue
	// fetching a random subset of records from the original batch, plus a new
	// ID. By examining the logs, we should be able to see that the cache only
	// fetches the ID that wasn't present in the original batch, indicating that
	// the batch itself isn't part of the key.
	for i := 1; i <= 100; i++ {
		// Get N ids from the original batch.
		recordsToFetch := rand.IntN(10) + 1
		batch := make([]string, recordsToFetch)
		copy(batch, ids[:recordsToFetch])
		// Add a random ID between 1 and 100 to the batch.
		batch = append(batch, strconv.Itoa(rand.IntN(1000)+10))
		values, _ := api.GetBatch(context.Background(), batch)
		// Print the records we retrieved from the cache.
		log.Println(values)
	}
}

Running this code, we can see that we only end up fetching the randomized ID, while continuously getting cache hits for IDs 1-10, regardless of what the batch looks like:

2024/04/07 11:09:58 Seed completed
2024/04/07 11:09:58 Cache miss. Fetching ids: 173
2024/04/07 11:09:58 map[1:value 173:value 2:value 3:value 4:value]
2024/04/07 11:09:58 Cache miss. Fetching ids: 12
2024/04/07 11:09:58 map[1:value 12:value 2:value 3:value 4:value]
2024/04/07 11:09:58 Cache miss. Fetching ids: 730
2024/04/07 11:09:58 map[1:value 2:value 3:value 4:value 730:value]
2024/04/07 11:09:58 Cache miss. Fetching ids: 520
2024/04/07 11:09:58 map[1:value 2:value 3:value 4:value 5:value 520:value 6:value 7:value 8:value]
...

The entire example is available here.

Cache key permutations

If you're attempting to cache data from an upstream system, the ID alone may be insufficient to uniquely identify the record in your cache. The endpoint you're calling might accept a variety of options that transform the data in different ways.

Consider this:

curl https://movie-api/movies?ids=1,2,3&filterUpcoming=true&includeTrailers=false
curl https://movie-api/movies?ids=1,2,3&filterUpcoming=false&includeTrailers=true

The IDs might be enough to uniquely identify these records in a database. However, when you're consuming them through another system, they will probably appear completely different as transformations are applied based on the options you pass it. Hence, it's important that we store these records once for each unique option set.

The options does not have to be query parameters either. The data source you're consuming could still be a database, and the options that you want to make part of the cache key could be different types of filters.

Below is a small example application to showcase this functionality:

type OrderOptions struct {
	CarrierName        string
	LatestDeliveryTime string
}

type OrderAPI struct {
	*sturdyc.Client[string]
}

func NewOrderAPI(c *sturdyc.Client[string]) *OrderAPI {
	return &OrderAPI{c}
}

func (a *OrderAPI) OrderStatus(ctx context.Context, ids []string, opts OrderOptions) (map[string]string, error) {
	// We use the PermutedBatchKeyFn when an ID isn't enough to uniquely identify a
	// record. The cache is going to store each id once per set of options.
	cacheKeyFn := a.PermutatedBatchKeyFn("key", opts)

	// We'll create a fetchFn with a closure that captures the options. For this
	// simple example, it logs and returns the status for each order, but you could
	// just as easily have called an external API.
	fetchFn := func(_ context.Context, cacheMisses []string) (map[string]string, error) {
		log.Printf("Fetching: %v, carrier: %s, delivery time: %s\n", cacheMisses, opts.CarrierName, opts.LatestDeliveryTime)
		response := map[string]string{}
		for _, id := range cacheMisses {
			response[id] = fmt.Sprintf("Available for %s", opts.CarrierName)
		}
		return response, nil
	}
	return a.GetOrFetchBatch(ctx, ids, cacheKeyFn, fetchFn)
}

The main difference from the previous example is that we're using PermutatedBatchKeyFn instead of BatchKeyFn. Internally, the cache will use reflection to extract the names and values of every exported field in the opts struct, and then include them when it constructs the cache keys.

The struct should be flat without nesting. The fields can be time.Time values, as well as any basic types, pointers to these types, and slices containing them.

Now, let's try to use this client:

func main() {
	// ...

	// Create a new cache client with the specified configuration.
	cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
		sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, retryBaseDelay),
	)

	// We will fetch these IDs using three different option sets.
	ids := []string{"id1", "id2", "id3"}
	optionSetOne := OrderOptions{CarrierName: "FEDEX", LatestDeliveryTime: "2024-04-06"}
	optionSetTwo := OrderOptions{CarrierName: "DHL", LatestDeliveryTime: "2024-04-07"}
	optionSetThree := OrderOptions{CarrierName: "UPS", LatestDeliveryTime: "2024-04-08"}

	orderClient := NewOrderAPI(cacheClient)
	ctx := context.Background()

	// Next, we'll call the orderClient to make sure that we've retrieved and cached
	// these IDs for all of our option sets.
	log.Println("Filling the cache with all IDs for all option sets")
	orderClient.OrderStatus(ctx, ids, optionSetOne)
	orderClient.OrderStatus(ctx, ids, optionSetTwo)
	orderClient.OrderStatus(ctx, ids, optionSetThree)
	log.Println("Cache filled")
}

At this point, the cache has stored each record individually for each option set. We can imagine that the keys would look something like this:

FEDEX-2024-04-06-id1
DHL-2024-04-07-id1
UPS-2024-04-08-id1
etc..

Next, we'll add a sleep to make sure that all of the records are due for a refresh, and then request the ids individually for each set of options:

func main() {
	// ...

	// Sleep to make sure that all records are due for a refresh.
	time.Sleep(maxRefreshDelay + 1)

	// Fetch each id for each option set.
	for i := 0; i < len(ids); i++ {
		// NOTE: We're using the same ID for these requests.
		orderClient.OrderStatus(ctx, []string{ids[i]}, optionSetOne)
		orderClient.OrderStatus(ctx, []string{ids[i]}, optionSetTwo)
		orderClient.OrderStatus(ctx, []string{ids[i]}, optionSetThree)
	}

	// Sleep for a second to allow the refresh logs to print.
	time.Sleep(time.Second)
}

Running this program, we can see that the records are refreshed once per unique id+option combination:

go run .
2024/04/07 13:33:56 Filling the cache with all IDs for all option sets
2024/04/07 13:33:56 Fetching: [id1 id2 id3], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:33:56 Fetching: [id1 id2 id3], carrier: DHL, delivery time: 2024-04-07
2024/04/07 13:33:56 Fetching: [id1 id2 id3], carrier: UPS, delivery time: 2024-04-08
2024/04/07 13:33:56 Cache filled
2024/04/07 13:33:58 Fetching: [id1], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:33:58 Fetching: [id1], carrier: UPS, delivery time: 2024-04-08
2024/04/07 13:33:58 Fetching: [id1], carrier: DHL, delivery time: 2024-04-07
2024/04/07 13:33:58 Fetching: [id2], carrier: UPS, delivery time: 2024-04-08
2024/04/07 13:33:58 Fetching: [id2], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:33:58 Fetching: [id2], carrier: DHL, delivery time: 2024-04-07
2024/04/07 13:33:58 Fetching: [id3], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:33:58 Fetching: [id3], carrier: UPS, delivery time: 2024-04-08
2024/04/07 13:33:58 Fetching: [id3], carrier: DHL, delivery time: 2024-04-07

The entire example is available here.

Refresh coalescing

As seen in the example above, we're storing the records once for every set of options. However, we're not really utilizing the fact that the endpoint is batchable when we're performing the refreshes.

To make this more efficient, we can enable the refresh coalescing functionality. Internally, the cache is going to create a buffer for every cache key permutation. It is then going to collect ids until it reaches a certain size, or exceeds a time-based threshold.

The only change we have to make to the previous example is to enable this feature:

func main() {
	// ...

	// With refresh coalescing enabled, the cache will buffer refreshes
	// until the batch size is reached or the buffer timeout is hit.
	batchSize := 3
	batchBufferTimeout := time.Second * 30

	// Create a new cache client with the specified configuration.
	cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
		sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, retryBaseDelay),
		sturdyc.WithRefreshCoalescing(batchSize, batchBufferTimeout),
	)

	// ...
}

and now we can see that the cache performs the refreshes in batches per permutation of our query params:

go run .
2024/04/07 13:45:42 Filling the cache with all IDs for all option sets
2024/04/07 13:45:42 Fetching: [id1 id2 id3], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:45:42 Fetching: [id1 id2 id3], carrier: DHL, delivery time: 2024-04-07
2024/04/07 13:45:42 Fetching: [id1 id2 id3], carrier: UPS, delivery time: 2024-04-08
2024/04/07 13:45:42 Cache filled
2024/04/07 13:45:44 Fetching: [id1 id2 id3], carrier: FEDEX, delivery time: 2024-04-06
2024/04/07 13:45:44 Fetching: [id1 id3 id2], carrier: DHL, delivery time: 2024-04-07
2024/04/07 13:45:44 Fetching: [id1 id2 id3], carrier: UPS, delivery time: 2024-04-08

The number of outgoing requests for the refreshes went from 9 to 3. Imagine what a batch size of 50 would do for your applications performance!

The entire example is available here.

Passthrough

There are times when you want to always retrieve the latest data from the source and only use the in-memory cache as a fallback. In such scenarios, you can use the Passthrough and PassthroughBatch functions. The cache will still perform in-flight request tracking and deduplicate your requests.

Distributed storage

I think it's important to read the previous sections before jumping here in order to understand all the heavy lifting sturdyc does when it comes to creating cache keys, tracking in-flight requests, refreshing records in the background to improve latency, and buffering/coalescing requests to minimize the number of round trips to underlying data sources.

Adding distributed storage to the cache is, from the package's point of view, essentially just another data source with a higher priority. Hence, we're still able to take great advantage of all the features we've seen so far, and these efficiency gains will hopefully allow you to use a much cheaper cluster.

Slightly simplified, we can think of the cache's interaction with the distributed storage like this:

// NOTE: This is an example. The cache has this functionality internally.
func (o *OrderAPI) OrderStatus(ctx context.Context, id string) (string, error) {
	cacheKey := "order-status-" + id
	fetchFn := func(ctx context.Context) (string, error) {
		// Check redis cache first.
		if orderStatus, ok := o.redisClient.Get(cacheKey); ok {
			return orderStatus, nil
		}

		// Fetch the order status from the underlying data source.
		var response OrderStatusResponse
		err := requests.URL(o.baseURL).
			Param("id", id).
			ToJSON(&response).
			Fetch(ctx)
		if err != nil {
			return "", err
		}

		// Add the order status to the redis cache.
		go func() { o.RedisClient.Set(cacheKey, response.OrderStatus, time.Hour) }()

		return response.OrderStatus, nil
	}

	return o.GetOrFetch(ctx, id, fetchFn)
}

Syncing the keys and values to a distributed storage like this can be highly beneficial, especially when we're deploying new containers where the in-memory cache will be empty, as it prevents sudden bursts of traffic to the underlying data sources.

Keeping the in-memory caches in sync with a distributed storage requires a bit more work though. sturdyc has therefore been designed to work with an abstraction that could represent any key-value store of your choosing, all you have to do is implement this interface:

type DistributedStorage interface {
	Get(ctx context.Context, key string) ([]byte, bool)
	Set(ctx context.Context, key string, value []byte)
	GetBatch(ctx context.Context, keys []string) map[string][]byte
	SetBatch(ctx context.Context, records map[string][]byte)
}

and then pass it to the WithDistributedStorage option when you create your cache client:

cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
	sturdyc.WithDistributedStorage(storage),
)

Please note that you are responsible for configuring the TTL and eviction policies of this storage. sturdyc will only make sure that it's being kept up-to-date with the data it has in-memory.

I've included an example to showcase this functionality here.

When running that application, you should see output that looks something like this:

❯ go run .
2024/06/07 10:32:56 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:56 Fetching shipping options from the underlying data source
2024/06/07 10:32:56 The shipping options were retrieved successfully!
2024/06/07 10:32:56 Writing key shipping-options-1234-asc to the distributed storage
2024/06/07 10:32:56 The shipping options were retrieved successfully!
2024/06/07 10:32:57 The shipping options were retrieved successfully!
2024/06/07 10:32:57 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:57 The shipping options were retrieved successfully!
2024/06/07 10:32:57 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:57 The shipping options were retrieved successfully!
2024/06/07 10:32:57 The shipping options were retrieved successfully!
2024/06/07 10:32:57 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:58 The shipping options were retrieved successfully!
2024/06/07 10:32:58 The shipping options were retrieved successfully!
2024/06/07 10:32:58 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:58 The shipping options were retrieved successfully!
2024/06/07 10:32:58 Getting key shipping-options-1234-asc from the distributed storage
2024/06/07 10:32:58 The shipping options were retrieved successfully!

Above we can see that the underlying data source was only visited once, and the in-memory cache performed a background refresh from the distributed storage every 2 to 3 retrievals to ensure that it's being kept up-to-date.

This sequence of events will repeat once the TTL expires.

Distributed storage early refreshes

Similar to the in-memory cache, we're also able to use a distributed storage where the data is refreshed before the TTL expires.

This would also allow us to serve stale data if an upstream was to experience any downtime:

cacheClient := sturdyc.New[string](capacity, numShards, ttl, evictionPercentage,
	sturdyc.WithDistributedStorageEarlyRefreshes(storage, time.Minute),
)

With the configuration above, we're essentially saying that we'd prefer if the data was refreshed once it's more than a minute old. However, if you're writing records with a 60 minute TTL, the cache will continously fallback to these if the refreshes were to fail, so the interaction with the distributed storage would look something like this:

• Start by trying to retrieve the key from the distributeted storage. If the data is fresh, it's returned immediately and written to the in-memory cache.
• If the key was found in the distributed storage, but wasn't fresh enough, we'll visit the underlying data source, and then write the response to both the distributed cache and the one we have in-memory.
• If the call to refresh the data failed, the cache will use the value from the distributed storage as a fallback.

However, there is one more scenario we must cover that requires two additional methods to be implemented:

type DistributedStorageEarlyRefreshes interface {
	DistributedStorage
	Delete(ctx context.Context, key string)
	DeleteBatch(ctx context.Context, keys []string)
}

These delete methods will be called when a refresh occurs, and the cache notices that it can no longer find the key at the underlying data source. This indicates that the key has been deleted, and we will want this change to propagate to the distributed key-value store

Please note that you are still responsible for setting the TTL and eviction policies for the distributed store. The cache will only invoke the delete methods when a record has gone missing from the underlying data source. If you're using missing record storage, it will write the key as a missing record instead.

I've included an example to showcase this functionality here.

Custom metrics

The cache can be configured to report custom metrics for:

• Size of the cache
• Cache hits
• Cache misses
• Evictions
• Forced evictions
• The number of entries evicted
• Shard distribution
• The size of the refresh buckets

There are also distributed metrics if you're using the cache with a distributed storage, which adds the following metrics in addition to what we've seen above:

• Distributed cache hits
• Distributed cache misses
• Distributed stale fallback

All you have to do is implement one of these interfaces:

type MetricsRecorder interface {
	CacheMiss()
	Eviction()
	ForcedEviction()
	EntriesEvicted(int)
	ShardIndex(int)
	CacheBatchRefreshSize(size int)
	ObserveCacheSize(callback func() int)
}

type DistributedMetricsRecorder interface {
	MetricsRecorder
	DistributedCacheHit()
	DistributedCacheMiss()
	DistributedFallback()
}

and pass it as an option when you create the client:

cacheBasicMetrics := sturdyc.New[any](
	cacheSize,
	shardSize,
	cacheTTL,
	evictWhenFullPercentage,
	sturdyc.WithMetrics(metricsRecorder),
)

cacheDistributedMetrics := sturdyc.New[any](
	cacheSize,
	shardSize,
	cacheTTL,
	evictWhenFullPercentage,
	sturdyc.WithDistributedStorage(metricsRecorder),
	sturdyc.WithDistributedMetrics(metricsRecorder),
)

Below are a few images where these metrics have been visualized in Grafana:

Here we can how often we're able to serve from memory. This image displays the number of items we have cached. This chart shows the batch sizes for the buffered refreshes. And lastly, we can see the average batch size of our refreshes for two different data sources.

You are also able to visualize evictions, forced evictions which occur when the cache has reached its capacity, as well as the distribution between the shards.

Generics

Personally, I tend to create caches based on how frequently the data needs to be refreshed rather than what type of data it stores. I'll often have one transient cache which refreshes the data every 2-5 milliseconds, and another cache where I'm fine if the data is up to a minute old.

Hence, I don't want to tie the cache to any specific type so I'll often just use any:

	cacheClient := sturdyc.New[any](capacity, numShards, ttl, evictionPercentage,
		sturdyc.WithEarlyRefreshes(minRefreshDelay, maxRefreshDelay, retryBaseDelay),
		sturdyc.WithRefreshCoalescing(10, time.Second*15),
	)

However, having all client methods return any can quickly add a lot of boilerplate if you're storing more than a handful of types, and need to make type assertions.

If you want to avoid this, you can use any of the package level exports:

• GetOrFetch
• GetOrFetchBatch
• Passthrough
• PassthroughBatch

They will take the cache, call the function for you, and perform the type conversions internally. If the type conversions were to fail, you'll get a ErrInvalidType error.

Below is an example of what an API client that uses these functions could look like:

type OrderAPI struct {
	cacheClient *sturdyc.Client[any]
}

func NewOrderAPI(c *sturdyc.Client[any]) *OrderAPI {
	return &OrderAPI{cacheClient: c}
}

func (a *OrderAPI) OrderStatus(ctx context.Context, ids []string) (map[string]string, error) {
	cacheKeyFn := a.cacheClient.BatchKeyFn("order-status")
	fetchFn := func(_ context.Context, cacheMisses []string) (map[string]string, error) {
		response := make(map[string]string, len(ids))
		for _, id := range cacheMisses {
			response[id] = "Order status: pending"
		}
		return response, nil
	}
	return sturdyc.GetOrFetchBatch(ctx, a.cacheClient, ids, cacheKeyFn, fetchFn)
}

func (a *OrderAPI) DeliveryTime(ctx context.Context, ids []string) (map[string]time.Time, error) {
	cacheKeyFn := a.cacheClient.BatchKeyFn("delivery-time")
	fetchFn := func(_ context.Context, cacheMisses []string) (map[string]time.Time, error) {
		response := make(map[string]time.Time, len(ids))
		for _, id := range cacheMisses {
			response[id] = time.Now()
		}
		return response, nil
	}
	return sturdyc.GetOrFetchBatch(ctx, a.cacheClient, ids, cacheKeyFn, fetchFn)
}

The entire example is available here.