This package contains a high-performance, columnar, in-memory storage engine that supports fast querying, update and iteration with zero-allocations and bitmap indexing.
- Optimized, cache-friendly columnar data layout that minimizes cache-misses.
- Optimized for zero heap allocation during querying (see benchmarks below).
- Optimized batch updates/deletes, an update during a transaction takes around
12ns. - Support for SIMD-enabled filtering (i.e. "where" clause) by leveraging bitmap indexing.
- Support for columnar projection (i.e. "select" clause) for fast retrieval.
- Support for computed indexes that are dynamically calculated based on provided predicate.
- Support for concurrent updates per-column (e.g. 2 goroutines can update 2 columns concurrently).
- Support for transaction isolation, allowing you to create transactions and commit/rollback.
- Support for expiration of rows based on time-to-live or expiration column.
- Support for atomic increment/decrement of numerical values, transactionally.
- Support for change data stream that streams all commits consistently.
The general idea is to leverage cache-friendly ways of organizing data in structures of arrays (SoA) otherwise known "columnar" storage in database design. This, in turn allows us to iterate and filter over columns very efficiently. On top of that, this package also adds bitmap indexing to the columnar storage, allowing to build filter queries using binary and, and not, or and xor (see kelindar/bitmap with SIMD support).
- Collection and Columns
- Querying and Indexing
- Iterating over Results
- Updating Values
- Expiring Values
- Transaction Commit and Rollback
- Streaming Changes
- Complete Example
- Benchmarks
- Contributing
In order to get data into the store, you'll need to first create a Collection by calling NewCollection() method. Each collection requires a schema, which can be either specified manually by calling CreateColumn() multiple times or automatically inferred from an object by calling CreateColumnsOf() function.
In the example below we're loading some JSON data by using json.Unmarshal() and auto-creating colums based on the first element on the loaded slice. After this is done, we can then load our data by inserting the objects one by one into the collection. This is accomplished by calling Insert() method on the collection itself repeatedly.
data := loadFromJson("players.json")
// Create a new columnar collection
players := column.NewCollection()
players.CreateColumnsOf(data[0])
// Insert every item from our loaded data
for _, v := range data {
players.Insert(v)
}Now, let's say we only want specific columns to be added. We can do this by calling CreateColumn() method on the collection manually to create the required columns.
// Create a new columnar collection with pre-defined columns
players := column.NewCollection()
players.CreateColumn("name", column.ForString())
players.CreateColumn("class", column.ForString())
players.CreateColumn("balance", column.ForFloat64())
players.CreateColumn("age", column.ForInt16())
// Insert every item from our loaded data
for _, v := range loadFromJson("players.json") {
players.Insert(v)
}While the previous example demonstrated how to insert many objects, it was doing it one by one and is rather inefficient. This is due to the fact that each Insert() call directly on the collection initiates a separate transacion and there's a small performance cost associated with it. If you want to do a bulk insert and insert many values, faster, that can be done by calling Insert() on a transaction, as demonstrated in the example below. Note that the only difference is instantiating a transaction by calling the Query() method and calling the txn.Insert() method on the transaction instead the one on the collection.
players.Query(func(txn *Txn) error {
for _, v := range loadFromJson("players.json") {
txn.Insert(v)
}
return nil // Commit
})The store allows you to query the data based on a presence of certain attributes or their values. In the example below we are querying our collection and applying a filtering operation bu using WithValue() method on the transaction. This method scans the values and checks whether a certain predicate evaluates to true. In this case, we're scanning through all of the players and looking up their class, if their class is equal to "rogue", we'll take it. At the end, we're calling Count() method that simply counts the result set.
// This query performs a full scan of "class" column
players.Query(func(txn *column.Txn) error {
count := txn.WithValue("class", func(v interface{}) bool {
return v == "rogue"
}).Count()
return nil
})Now, what if we'll need to do this query very often? It is possible to simply create an index with the same predicate and have this computation being applied every time (a) an object is inserted into the collection and (b) an value of the dependent column is updated. Let's look at the example below, we're fist creating a rogue index which depends on "class" column. This index applies the same predicate which only returns true if a class is "rogue". We then can query this by simply calling With() method and providing the index name.
An index is essentially akin to a boolean column, so you could technically also select it's value when querying it. Now, in this example the query would be around 10-100x faster to execute as behind the scenes it uses bitmap indexing for the "rogue" index and performs a simple logical AND operation on two bitmaps when querying. This avoid the entire scanning and applying of a predicate during the Query.
// Create the index "rogue" in advance
out.CreateIndex("rogue", "class", func(v interface{}) bool {
return v == "rogue"
})
// This returns the same result as the query before, but much faster
players.Query(func(txn *column.Txn) error {
count := txn.With("rogue").Count()
return nil
})The query can be further expanded as it allows indexed intersection, difference and union operations. This allows you to ask more complex questions of a collection. In the examples below let's assume we have a bunch of indexes on the class column and we want to ask different questions.
First, let's try to merge two queries by applying a Union() operation with the method named the same. Here, we first select only rogues but then merge them together with mages, resulting in selection containing both rogues and mages.
// How many rogues and mages?
players.Query(func(txn *Txn) error {
txn.With("rogue").Union("mage").Count()
return nil
})Next, let's count everyone who isn't a rogue, for that we can use a Without() method which performs a difference (i.e. binary AND NOT operation) on the collection. This will result in a count of all players in the collection except the rogues.
// How many rogues and mages?
players.Query(func(txn *Txn) error {
txn.Without("rogue").Count()
return nil
})Now, you can combine all of the methods and keep building more complex queries. When querying indexed and non-indexed fields together it is important to know that as every scan will apply to only the selection, speeding up the query. So if you have a filter on a specific index that selects 50% of players and then you perform a scan on that (e.g. WithValue()), it will only scan 50% of users and hence will be 2x faster.
// How many rogues that are over 30 years old?
players.Query(func(txn *Txn) error {
txn.With("rogue").WithFloat("age", func(v float64) bool {
return v >= 30
}).Count()
return nil
})In all of the previous examples, we've only been doing Count() operation which counts the number of elements in the result set. In this section we'll look how we can iterate over the result set. In short, there's 2 main methods that allow us to do it:
Range()method which takes in a column name as an argument and allows faster get/set of the values for that column.Select()method which doesn't pre-select any specific column, so it's usually a bit slower and it also does not allow any updates.
Let's first examine the Range() method. In the example below we select all of the rogues from our collection and print out their name by using the Range() method and providing "name" column to it. The callback containing the Cursor allows us to quickly get the value of the column by calling String() method to retrieve a string value. It also contains methods such as Int(), Uint(), Float() or more generic Value() to pull data of different types.
players.Query(func(txn *Txn) error {
txn.With("rogue").Range("name", func(v column.Cursor) bool {
println("rogue name ", v.String()) // Prints the name
return true
})
return nil
})Now, what if you need two columns? The range only allows you to quickly select a single column, but you can still retrieve other columns by their name during the iteration. This can be accomplished by corresponding StringAt(), FloatAt(), IntAt(), UintAt() or ValueAt() methods as shown below.
players.Query(func(txn *Txn) error {
txn.With("rogue").Range("name", func(v column.Cursor) bool {
println("rogue name ", v.String()) // Prints the name
println("rogue age ", v.IntAt("age")) // Prints the age
return true
})
return nil
})On the other hand, Select() allows you to do a read-only selection which provides a Selector cursor. This cursor does not allow any updates, deletes or inserts and is also not pre-select any particular column. In the example below we print out names of all of the rogues using a selector.
players.Query(func(txn *Txn) error {
txn.With("rogue").Select(func(v column.Selector) bool {
println("rogue name ", v.StringAt("name")) // Prints the name
return true
})
return nil
})Now, what if you need to quickly delete all some of the data in the collection? In this case DeleteAll() or DeleteIf() methods come in handy. These methods are very fast (especially DeleteAll()) and allow you to quickly delete the appropriate results, transactionally. In the example below we delete all of the rogues from the collection by simply selecting them in the transaction and calling the DeleteAll() method.
players.Query(func(txn *Txn) error {
txn.With("rogue").DeleteAll()
return nil
})In order to update certain items in the collection, you can simply call Range() method and the corresponding Cursor's Update() or UpdateAt() methods that allow to update a value of a certain column atomically. The updates won't be directly reflected given that the store supports transactions and only when transaction is commited, then the update will be applied to the collection. This allows for isolation and rollbacks.
In the example below we're selecting all of the rogues and updating both their balance and age to certain values. The transaction returns nil, hence it will be automatically committed when Query() method returns.
players.Query(func(txn *Txn) error {
txn.With("rogue").Range("balance", func(v column.Cursor) bool {
v.Update(10.0) // Update the "balance" to 10.0
v.UpdateAt("age", 50) // Update the "age" to 50
return true
}) // Select the balance
return nil
})In certain cases, you might want to atomically increment or decrement numerical values. In order to accomplish this you can use the provided Add() or AddAt() operations of the Cursor or Selector. Note that the indexes will also be updated accordingly and the predicates re-evaluated with the most up-to-date values. In the below example we're incrementing the balance of all our rogues by 500 atomically.
players.Query(func(txn *Txn) error {
txn.With("rogue").Range("balance", func(v column.Cursor) bool {
v.Add(500.0) // Increment the "balance" by 500
return true
})
return nil
})Sometimes, it is useful to automatically delete certain rows when you do not need them anymore. In order to do this, the library automatically adds an expire column to each new collection and starts a cleanup goroutine aynchronously that runs periodically and cleans up the expired objects. In order to set this, you can simply use InsertWithTTL() method on the collection that allows to insert an object with a time-to-live duration defined.
In the example below we are inserting an object to the collection and setting the time-to-live to 5 seconds from the current time. After this time, the object will be automatically evicted from the collection and its space can be reclaimed.
players.InsertWithTTL(map[string]interface{}{
"name": "Merlin",
"class": "mage",
"age": 55,
"balance": 500,
}, 5 * time.Second) // The time-to-live of 5 secondsOn an interestig node, since expire column which is automatically added to each collection is an actual normal column, you can query and even update it. In the example below we query and conditionally update the expiration column. The example loads a time, adds one hour and updates it, but in practice if you want to do it you should use Add() method which can perform this atomically.
players.Query(func(txn *column.Txn) error {
return txn.Range("expire", func(v column.Cursor) bool {
oldExpire := time.Unix(0, v.Int()) // Convert expiration to time.Time
newExpire := expireAt.Add(1 * time.Hour).UnixNano() // Add some time
v.Update(newExpire)
return true
})
})Transactions allow for isolation between two concurrent operations. In fact, all of the batch queries must go through a transaction in this library. The Query method requires a function which takes in a column.Txn pointer which contains various helper methods that support querying. In the example below we're trying to iterate over all of the players and update their balance by setting it to 10.0. The Query method automatically calls txn.Commit() if the function returns without any error. On the flip side, if the provided function returns an error, the query will automatically call txn.Rollback() so none of the changes will be applied.
// Range over all of the players and update (successfully their balance)
players.Query(func(txn *column.Txn) error {
txn.Range("balance", func(v column.Cursor) bool {
v.Update(10.0) // Update the "balance" to 10.0
return true
})
// No error, transaction will be committed
return nil
})Now, in this example, we try to update balance but a query callback returns an error, in which case none of the updates will be actually reflected in the underlying collection.
// Range over all of the players and update (successfully their balance)
players.Query(func(txn *column.Txn) error {
txn.Range("balance", func(v column.Cursor) bool {
v.Update(10.0) // Update the "balance" to 10.0
return true
})
// Returns an error, transaction will be rolled back
return fmt.Errorf("bug")
})This library also supports streaming out all transaction commits consistently, as they happen. This allows you to implement your own change data capture (CDC) listeners, stream data into kafka or into a remote database for durability. In order to enable it, you can simply provide an implementation of a commit.Writer interface during the creation of the collection.
In the example below we take advantage of the commit.Channel implementation of a commit.Writer which simply publishes the commits into a go channel. Here we create a buffered channel and keep consuming the commits with a separate goroutine, allowing us to view transactions as they happen in the store.
// Create a new commit writer (simple channel) and a new collection
writer := make(commit.Channel, 1024)
players := NewCollection(column.Options{
Writer: writer,
})
// Read the changes from the channel
go func(){
for commit := writer{
println("commit", commit.Type.String())
}
}()
// ... insert, update or deleteOn a separate note, this change stream is guaranteed to be consistent and serialized. This means that you can also replicate those changes on another database and synchronize both. In fact, this library also provides Replay() method on the collection that allows to do just that. In the example below we create two collections primary and replica and asychronously replicating all of the commits from the primary to the replica using the Replay() method together with the change stream.
// Create a p rimary collection
writer := make(commit.Channel, 1024)
primary := column.NewCollection(column.Options{
Writer: &writer,
})
primary.CreateColumnsOf(object)
// Replica with the same schema
replica := column.NewCollection()
replica.CreateColumnsOf(object)
// Keep 2 collections in sync
go func() {
for change := range writer {
replica.Replay(change)
}
}()func main(){
// Create a new columnar collection
players := column.NewCollection()
// index on humans
players.CreateIndex("human", "race", func(v interface{}) bool {
return v == "human"
})
// index for mages
players.CreateIndex("mage", "class", func(v interface{}) bool {
return v == "mage"
})
// index for old
players.CreateIndex("old", "age", func(v interface{}) bool {
return v.(float64) >= 30
})
// Load the items into the collection
loaded := loadFixture("players.json")
players.CreateColumnsOf(loaded[0])
for _, v := range loaded {
players.Insert(v)
}
// This performs a full scan on 3 different columns and compares them given the
// specified predicates. This is not indexed, but does a columnar scan which is
// cache-friendly.
players.Query(func(txn *column.Txn) error {
println(txn.WithString("race", func(v string) bool {
return v == "human"
}).WithString("class", func(v string) bool {
return v == "mage"
}).WithFloat("age", func(v float64) bool {
return v >= 30
}).Count()) // prints the count
return nil
})
// This performs a cound, but instead of scanning through the entire dataset, it scans
// over pre-built indexes and combines them using a logical AND operation. The result
// will be the same as the query above but the performance of the query is 10x-100x
// faster depending on the size of the underlying data.
players.Query(func(txn *column.Txn) error {
println(txn.With("human", "mage", "old").Count()) // prints the count
return nil
})
// Same condition as above, but we also select the actual names of those
// players and iterate through them.
players.Query(func(txn *column.Txn) error {
txn.With("human", "mage", "old").Range("name", func(v column.Cursor) bool {
println(v.String()) // prints the name
return true
}) // The column to select
return nil
})
}The benchmarks below were ran on a collection of 100,000 items containing a dozen columns. Feel free to explore the benchmarks but I strongly recommend testing it on your actual dataset.
cpu: Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz
BenchmarkCollection/insert-8 5545016 216.8 ns/op 18 B/op 0 allocs/op
BenchmarkCollection/fetch-8 27272726 43.61 ns/op 0 B/op 0 allocs/op
BenchmarkCollection/scan-8 648 1844623 ns/op 147 B/op 0 allocs/op
BenchmarkCollection/count-8 1000000 1107 ns/op 0 B/op 0 allocs/op
BenchmarkCollection/range-8 10000 102549 ns/op 9 B/op 0 allocs/op
BenchmarkCollection/update-at-8 4316584 280.7 ns/op 0 B/op 0 allocs/op
BenchmarkCollection/update-all-8 826 1379693 ns/op 53068 B/op 0 allocs/op
BenchmarkCollection/delete-at-8 7059126 169.1 ns/op 0 B/op 0 allocs/op
BenchmarkCollection/delete-all-8 196734 6294 ns/op 0 B/op 0 allocs/op
When testing for larger collections, I added a small example (see examples folder) and ran it with 20 million rows inserted, each entry has 12 columns and 4 indexes that need to be calculated, and a few queries and scans around them.
running insert of 20000000 rows...
-> insert took 38.6921853s
running full scan of age >= 30...
-> result = 10200000
-> full scan took 171.712196ms
running full scan of class == "rogue"...
-> result = 7160000
-> full scan took 199.24443ms
running indexed query of human mages...
-> result = 1360000
-> indexed query took 574µs
running indexed query of human female mages...
-> result = 640000
-> indexed query took 747.148µs
running update of balance of everyone...
-> updated 20000000 rows
-> update took 317.528908ms
running update of age of mages...
-> updated 6040000 rows
-> update took 98.655836ms
We are open to contributions, feel free to submit a pull request and we'll review it as quickly as we can. This library is maintained by Roman Atachiants
Tile is licensed under the MIT License.