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BadgerDB GoDoc Go Report Card Sourcegraph Build Status Appveyor Coverage Status

Badger mascot

BadgerDB is an embeddable, persistent and fast key-value (KV) database written in pure Go. It is the underlying database for Dgraph, a fast, distributed graph database. It's meant to be a performant alternative to non-Go-based key-value stores like RocksDB.

Project Status [Jun 26, 2019]

Badger is stable and is being used to serve data sets worth hundreds of terabytes. Badger supports concurrent ACID transactions with serializable snapshot isolation (SSI) guarantees. A Jepsen-style bank test runs nightly for 8h, with --race flag and ensures the maintenance of transactional guarantees. Badger has also been tested to work with filesystem level anomalies, to ensure persistence and consistency.

Badger v1.0 was released in Nov 2017, and the latest version that is data-compatible with v1.0 is v1.6.0.

Badger v2.0, a new release coming up very soon will use a new storage format which won't be compatible with all of the v1.x. The Changelog is kept fairly up-to-date.

For more details on our version naming schema please read Choosing a version.

Table of Contents

Getting Started

Installing

To start using Badger, install Go 1.11 or above and run go get:

$ go get github.com/dgraph-io/badger/...

This will retrieve the library and install the badger command line utility into your $GOBIN path.

Note: Badger does not directly use CGO but it relies on https://github.com/DataDog/zstd for compression and it requires gcc/cgo. If you wish to use badger without gcc/cgo, you can run CGO_ENABLED=0 go get github.com/dgraph-io/badger/... which will download badger without the support for ZSTD compression algorithm.

Choosing a version

BadgerDB is a pretty special package from the point of view that the most important change we can make to it is not on its API but rather on how data is stored on disk.

This is why we follow a version naming schema that differs from Semantic Versioning.

  • New major versions are released when the data format on disk changes in an incompatible way.
  • New minor versions are released whenever the API changes but data compatibility is maintained. Note that the changes on the API could be backward-incompatible - unlike Semantic Versioning.
  • New patch versions are released when there's no changes to the data format nor the API.

Following these rules:

  • v1.5.0 and v1.6.0 can be used on top of the same files without any concerns, as their major version is the same, therefore the data format on disk is compatible.
  • v1.6.0 and v2.0.0 are data incompatible as their major version implies, so files created with v1.6.0 will need to be converted into the new format before they can be used by v2.0.0.

For a longer explanation on the reasons behind using a new versioning naming schema, you can read VERSIONING.md.

Opening a database

The top-level object in Badger is a DB. It represents multiple files on disk in specific directories, which contain the data for a single database.

To open your database, use the badger.Open() function, with the appropriate options. The Dir and ValueDir options are mandatory and must be specified by the client. They can be set to the same value to simplify things.

package main

import (
	"log"

	badger "github.com/dgraph-io/badger"
)

func main() {
  // Open the Badger database located in the /tmp/badger directory.
  // It will be created if it doesn't exist.
  db, err := badger.Open(badger.DefaultOptions("/tmp/badger"))
  if err != nil {
	  log.Fatal(err)
  }
  defer db.Close()
  // Your code here…
}

Please note that Badger obtains a lock on the directories so multiple processes cannot open the same database at the same time.

In-Memory Mode/Diskless Mode

By default, Badger ensures all the data is persisted to the disk. It also supports a pure in-memory mode. When Badger is running in in-memory mode, all the data is stored in the memory. Reads and writes are much faster in in-memory mode, but all the data stored in Badger will be lost in case of a crash or close. To open badger in in-memory mode, set the InMemory option.

opt := badger.DefaultOptions("").WithInMemory(true)

Transactions

Read-only transactions

To start a read-only transaction, you can use the DB.View() method:

err := db.View(func(txn *badger.Txn) error {
  // Your code here…
  return nil
})

You cannot perform any writes or deletes within this transaction. Badger ensures that you get a consistent view of the database within this closure. Any writes that happen elsewhere after the transaction has started, will not be seen by calls made within the closure.

Read-write transactions

To start a read-write transaction, you can use the DB.Update() method:

err := db.Update(func(txn *badger.Txn) error {
  // Your code here…
  return nil
})

All database operations are allowed inside a read-write transaction.

Always check the returned error value. If you return an error within your closure it will be passed through.

An ErrConflict error will be reported in case of a conflict. Depending on the state of your application, you have the option to retry the operation if you receive this error.

An ErrTxnTooBig will be reported in case the number of pending writes/deletes in the transaction exceeds a certain limit. In that case, it is best to commit the transaction and start a new transaction immediately. Here is an example (we are not checking for errors in some places for simplicity):

updates := make(map[string]string)
txn := db.NewTransaction(true)
for k,v := range updates {
  if err := txn.Set([]byte(k),[]byte(v)); err == badger.ErrTxnTooBig {
    _ = txn.Commit()
    txn = db.NewTransaction(true)
    _ = txn.Set([]byte(k),[]byte(v))
  }
}
_ = txn.Commit()

Managing transactions manually

The DB.View() and DB.Update() methods are wrappers around the DB.NewTransaction() and Txn.Commit() methods (or Txn.Discard() in case of read-only transactions). These helper methods will start the transaction, execute a function, and then safely discard your transaction if an error is returned. This is the recommended way to use Badger transactions.

However, sometimes you may want to manually create and commit your transactions. You can use the DB.NewTransaction() function directly, which takes in a boolean argument to specify whether a read-write transaction is required. For read-write transactions, it is necessary to call Txn.Commit() to ensure the transaction is committed. For read-only transactions, calling Txn.Discard() is sufficient. Txn.Commit() also calls Txn.Discard() internally to cleanup the transaction, so just calling Txn.Commit() is sufficient for read-write transaction. However, if your code doesn’t call Txn.Commit() for some reason (for e.g it returns prematurely with an error), then please make sure you call Txn.Discard() in a defer block. Refer to the code below.

// Start a writable transaction.
txn := db.NewTransaction(true)
defer txn.Discard()

// Use the transaction...
err := txn.Set([]byte("answer"), []byte("42"))
if err != nil {
    return err
}

// Commit the transaction and check for error.
if err := txn.Commit(); err != nil {
    return err
}

The first argument to DB.NewTransaction() is a boolean stating if the transaction should be writable.

Badger allows an optional callback to the Txn.Commit() method. Normally, the callback can be set to nil, and the method will return after all the writes have succeeded. However, if this callback is provided, the Txn.Commit() method returns as soon as it has checked for any conflicts. The actual writing to the disk happens asynchronously, and the callback is invoked once the writing has finished, or an error has occurred. This can improve the throughput of the application in some cases. But it also means that a transaction is not durable until the callback has been invoked with a nil error value.

Using key/value pairs

To save a key/value pair, use the Txn.Set() method:

err := db.Update(func(txn *badger.Txn) error {
  err := txn.Set([]byte("answer"), []byte("42"))
  return err
})

Key/Value pair can also be saved by first creating Entry, then setting this Entry using Txn.SetEntry(). Entry also exposes methods to set properties on it.

err := db.Update(func(txn *badger.Txn) error {
  e := badger.NewEntry([]byte("answer"), []byte("42"))
  err := txn.SetEntry(e)
  return err
})

This will set the value of the "answer" key to "42". To retrieve this value, we can use the Txn.Get() method:

err := db.View(func(txn *badger.Txn) error {
  item, err := txn.Get([]byte("answer"))
  handle(err)

  var valNot, valCopy []byte
  err := item.Value(func(val []byte) error {
    // This func with val would only be called if item.Value encounters no error.

    // Accessing val here is valid.
    fmt.Printf("The answer is: %s\n", val)

    // Copying or parsing val is valid.
    valCopy = append([]byte{}, val...)

    // Assigning val slice to another variable is NOT OK.
    valNot = val // Do not do this.
    return nil
  })
  handle(err)

  // DO NOT access val here. It is the most common cause of bugs.
  fmt.Printf("NEVER do this. %s\n", valNot)

  // You must copy it to use it outside item.Value(...).
  fmt.Printf("The answer is: %s\n", valCopy)

  // Alternatively, you could also use item.ValueCopy().
  valCopy, err = item.ValueCopy(nil)
  handle(err)
  fmt.Printf("The answer is: %s\n", valCopy)

  return nil
})

Txn.Get() returns ErrKeyNotFound if the value is not found.

Please note that values returned from Get() are only valid while the transaction is open. If you need to use a value outside of the transaction then you must use copy() to copy it to another byte slice.

Use the Txn.Delete() method to delete a key.

Monotonically increasing integers

To get unique monotonically increasing integers with strong durability, you can use the DB.GetSequence method. This method returns a Sequence object, which is thread-safe and can be used concurrently via various goroutines.

Badger would lease a range of integers to hand out from memory, with the bandwidth provided to DB.GetSequence. The frequency at which disk writes are done is determined by this lease bandwidth and the frequency of Next invocations. Setting a bandwidth too low would do more disk writes, setting it too high would result in wasted integers if Badger is closed or crashes. To avoid wasted integers, call Release before closing Badger.

seq, err := db.GetSequence(key, 1000)
defer seq.Release()
for {
  num, err := seq.Next()
}

Merge Operations

Badger provides support for ordered merge operations. You can define a func of type MergeFunc which takes in an existing value, and a value to be merged with it. It returns a new value which is the result of the merge operation. All values are specified in byte arrays. For e.g., here is a merge function (add) which appends a []byte value to an existing []byte value.

// Merge function to append one byte slice to another
func add(originalValue, newValue []byte) []byte {
  return append(originalValue, newValue...)
}

This function can then be passed to the DB.GetMergeOperator() method, along with a key, and a duration value. The duration specifies how often the merge function is run on values that have been added using the MergeOperator.Add() method.

MergeOperator.Get() method can be used to retrieve the cumulative value of the key associated with the merge operation.

key := []byte("merge")

m := db.GetMergeOperator(key, add, 200*time.Millisecond)
defer m.Stop()

m.Add([]byte("A"))
m.Add([]byte("B"))
m.Add([]byte("C"))

res, _ := m.Get() // res should have value ABC encoded

Example: Merge operator which increments a counter

func uint64ToBytes(i uint64) []byte {
  var buf [8]byte
  binary.BigEndian.PutUint64(buf[:], i)
  return buf[:]
}

func bytesToUint64(b []byte) uint64 {
  return binary.BigEndian.Uint64(b)
}

// Merge function to add two uint64 numbers
func add(existing, new []byte) []byte {
  return uint64ToBytes(bytesToUint64(existing) + bytesToUint64(new))
}

It can be used as

key := []byte("merge")

m := db.GetMergeOperator(key, add, 200*time.Millisecond)
defer m.Stop()

m.Add(uint64ToBytes(1))
m.Add(uint64ToBytes(2))
m.Add(uint64ToBytes(3))

res, _ := m.Get() // res should have value 6 encoded

Setting Time To Live(TTL) and User Metadata on Keys

Badger allows setting an optional Time to Live (TTL) value on keys. Once the TTL has elapsed, the key will no longer be retrievable and will be eligible for garbage collection. A TTL can be set as a time.Duration value using the Entry.WithTTL() and Txn.SetEntry() API methods.

err := db.Update(func(txn *badger.Txn) error {
  e := badger.NewEntry([]byte("answer"), []byte("42")).WithTTL(time.Hour)
  err := txn.SetEntry(e)
  return err
})

An optional user metadata value can be set on each key. A user metadata value is represented by a single byte. It can be used to set certain bits along with the key to aid in interpreting or decoding the key-value pair. User metadata can be set using Entry.WithMeta() and Txn.SetEntry() API methods.

err := db.Update(func(txn *badger.Txn) error {
  e := badger.NewEntry([]byte("answer"), []byte("42")).WithMeta(byte(1))
  err := txn.SetEntry(e)
  return err
})

Entry APIs can be used to add the user metadata and TTL for same key. This Entry then can be set using Txn.SetEntry().

err := db.Update(func(txn *badger.Txn) error {
  e := badger.NewEntry([]byte("answer"), []byte("42")).WithMeta(byte(1)).WithTTL(time.Hour)
  err := txn.SetEntry(e)
  return err
})

Iterating over keys

To iterate over keys, we can use an Iterator, which can be obtained using the Txn.NewIterator() method. Iteration happens in byte-wise lexicographical sorting order.

err := db.View(func(txn *badger.Txn) error {
  opts := badger.DefaultIteratorOptions
  opts.PrefetchSize = 10
  it := txn.NewIterator(opts)
  defer it.Close()
  for it.Rewind(); it.Valid(); it.Next() {
    item := it.Item()
    k := item.Key()
    err := item.Value(func(v []byte) error {
      fmt.Printf("key=%s, value=%s\n", k, v)
      return nil
    })
    if err != nil {
      return err
    }
  }
  return nil
})

The iterator allows you to move to a specific point in the list of keys and move forward or backward through the keys one at a time.

By default, Badger prefetches the values of the next 100 items. You can adjust that with the IteratorOptions.PrefetchSize field. However, setting it to a value higher than GOMAXPROCS (which we recommend to be 128 or higher) shouldn’t give any additional benefits. You can also turn off the fetching of values altogether. See section below on key-only iteration.

Prefix scans

To iterate over a key prefix, you can combine Seek() and ValidForPrefix():

db.View(func(txn *badger.Txn) error {
  it := txn.NewIterator(badger.DefaultIteratorOptions)
  defer it.Close()
  prefix := []byte("1234")
  for it.Seek(prefix); it.ValidForPrefix(prefix); it.Next() {
    item := it.Item()
    k := item.Key()
    err := item.Value(func(v []byte) error {
      fmt.Printf("key=%s, value=%s\n", k, v)
      return nil
    })
    if err != nil {
      return err
    }
  }
  return nil
})

Key-only iteration

Badger supports a unique mode of iteration called key-only iteration. It is several order of magnitudes faster than regular iteration, because it involves access to the LSM-tree only, which is usually resident entirely in RAM. To enable key-only iteration, you need to set the IteratorOptions.PrefetchValues field to false. This can also be used to do sparse reads for selected keys during an iteration, by calling item.Value() only when required.

err := db.View(func(txn *badger.Txn) error {
  opts := badger.DefaultIteratorOptions
  opts.PrefetchValues = false
  it := txn.NewIterator(opts)
  defer it.Close()
  for it.Rewind(); it.Valid(); it.Next() {
    item := it.Item()
    k := item.Key()
    fmt.Printf("key=%s\n", k)
  }
  return nil
})

Stream

Badger provides a Stream framework, which concurrently iterates over all or a portion of the DB, converting data into custom key-values, and streams it out serially to be sent over network, written to disk, or even written back to Badger. This is a lot faster way to iterate over Badger than using a single Iterator. Stream supports Badger in both managed and normal mode.

Stream uses the natural boundaries created by SSTables within the LSM tree, to quickly generate key ranges. Each goroutine then picks a range and runs an iterator to iterate over it. Each iterator iterates over all versions of values and is created from the same transaction, thus working over a snapshot of the DB. Every time a new key is encountered, it calls ChooseKey(item), followed by KeyToList(key, itr). This allows a user to select or reject that key, and if selected, convert the value versions into custom key-values. The goroutine batches up 4MB worth of key-values, before sending it over to a channel. Another goroutine further batches up data from this channel using smart batching algorithm and calls Send serially.

This framework is designed for high throughput key-value iteration, spreading the work of iteration across many goroutines. DB.Backup uses this framework to provide full and incremental backups quickly. Dgraph is a heavy user of this framework. In fact, this framework was developed and used within Dgraph, before getting ported over to Badger.

stream := db.NewStream()
// db.NewStreamAt(readTs) for managed mode.

// -- Optional settings
stream.NumGo = 16                     // Set number of goroutines to use for iteration.
stream.Prefix = []byte("some-prefix") // Leave nil for iteration over the whole DB.
stream.LogPrefix = "Badger.Streaming" // For identifying stream logs. Outputs to Logger.

// ChooseKey is called concurrently for every key. If left nil, assumes true by default.
stream.ChooseKey = func(item *badger.Item) bool {
  return bytes.HasSuffix(item.Key(), []byte("er"))
}

// KeyToList is called concurrently for chosen keys. This can be used to convert
// Badger data into custom key-values. If nil, uses stream.ToList, a default
// implementation, which picks all valid key-values.
stream.KeyToList = nil

// -- End of optional settings.

// Send is called serially, while Stream.Orchestrate is running.
stream.Send = func(list *pb.KVList) error {
  return proto.MarshalText(w, list) // Write to w.
}

// Run the stream
if err := stream.Orchestrate(context.Background()); err != nil {
  return err
}
// Done.

Garbage Collection

Badger values need to be garbage collected, because of two reasons:

  • Badger keeps values separately from the LSM tree. This means that the compaction operations that clean up the LSM tree do not touch the values at all. Values need to be cleaned up separately.

  • Concurrent read/write transactions could leave behind multiple values for a single key, because they are stored with different versions. These could accumulate, and take up unneeded space beyond the time these older versions are needed.

Badger relies on the client to perform garbage collection at a time of their choosing. It provides the following method, which can be invoked at an appropriate time:

  • DB.RunValueLogGC(): This method is designed to do garbage collection while Badger is online. Along with randomly picking a file, it uses statistics generated by the LSM-tree compactions to pick files that are likely to lead to maximum space reclamation. It is recommended to be called during periods of low activity in your system, or periodically. One call would only result in removal of at max one log file. As an optimization, you could also immediately re-run it whenever it returns nil error (indicating a successful value log GC), as shown below.

     ticker := time.NewTicker(5 * time.Minute)
 defer ticker.Stop()
 for range ticker.C {
 again:
 	err := db.RunValueLogGC(0.7)
 	if err == nil {
 		goto again
 	}
 }

  • DB.PurgeOlderVersions(): This method is DEPRECATED since v1.5.0. Now, Badger's LSM tree automatically discards older/invalid versions of keys.

Note: The RunValueLogGC method would not garbage collect the latest value log.

Database backup

There are two public API methods DB.Backup() and DB.Load() which can be used to do online backups and restores. Badger v0.9 provides a CLI tool badger, which can do offline backup/restore. Make sure you have $GOPATH/bin in your PATH to use this tool.

The command below will create a version-agnostic backup of the database, to a file badger.bak in the current working directory

badger backup --dir <path/to/badgerdb>

To restore badger.bak in the current working directory to a new database:

badger restore --dir <path/to/badgerdb>

See badger --help for more details.

If you have a Badger database that was created using v0.8 (or below), you can use the badger_backup tool provided in v0.8.1, and then restore it using the command above to upgrade your database to work with the latest version.

badger_backup --dir <path/to/badgerdb> --backup-file badger.bak

We recommend all users to use the Backup and Restore APIs and tools. However, Badger is also rsync-friendly because all files are immutable, barring the latest value log which is append-only. So, rsync can be used as rudimentary way to perform a backup. In the following script, we repeat rsync to ensure that the LSM tree remains consistent with the MANIFEST file while doing a full backup.

#!/bin/bash
set -o history
set -o histexpand
# Makes a complete copy of a Badger database directory.
# Repeat rsync if the MANIFEST and SSTables are updated.
rsync -avz --delete db/ dst
while !! | grep -q "(MANIFEST\|\.sst)$"; do :; done

Memory usage

Badger's memory usage can be managed by tweaking several options available in the Options struct that is passed in when opening the database using DB.Open.

  • Options.ValueLogLoadingMode can be set to options.FileIO (instead of the default options.MemoryMap) to avoid memory-mapping log files. This can be useful in environments with low RAM.
  • Number of memtables (Options.NumMemtables)
    • If you modify Options.NumMemtables, also adjust Options.NumLevelZeroTables and Options.NumLevelZeroTablesStall accordingly.
  • Number of concurrent compactions (Options.NumCompactors)
  • Mode in which LSM tree is loaded (Options.TableLoadingMode)
  • Size of table (Options.MaxTableSize)
  • Size of value log file (Options.ValueLogFileSize)

If you want to decrease the memory usage of Badger instance, tweak these options (ideally one at a time) until you achieve the desired memory usage.

Statistics

Badger records metrics using the expvar package, which is included in the Go standard library. All the metrics are documented in y/metrics.go file.

expvar package adds a handler in to the default HTTP server (which has to be started explicitly), and serves up the metrics at the /debug/vars endpoint. These metrics can then be collected by a system like Prometheus, to get better visibility into what Badger is doing.

Resources

Blog Posts

  1. Introducing Badger: A fast key-value store written natively in Go
  2. Make Badger crash resilient with ALICE
  3. Badger vs LMDB vs BoltDB: Benchmarking key-value databases in Go
  4. Concurrent ACID Transactions in Badger

Design

Badger was written with these design goals in mind:

  • Write a key-value database in pure Go.
  • Use latest research to build the fastest KV database for data sets spanning terabytes.
  • Optimize for SSDs.

Badger’s design is based on a paper titled WiscKey: Separating Keys from Values in SSD-conscious Storage.

Comparisons

Feature Badger RocksDB BoltDB
Design LSM tree with value log LSM tree only B+ tree
High Read throughput Yes No Yes
High Write throughput Yes Yes No
Designed for SSDs Yes (with latest research 1) Not specifically 2 No
Embeddable Yes Yes Yes
Sorted KV access Yes Yes Yes
Pure Go (no Cgo) Yes No Yes
Transactions Yes, ACID, concurrent with SSI3 Yes (but non-ACID) Yes, ACID
Snapshots Yes Yes Yes
TTL support Yes Yes No
3D access (key-value-version) Yes4 No No

1 The WISCKEY paper (on which Badger is based) saw big wins with separating values from keys, significantly reducing the write amplification compared to a typical LSM tree.

2 RocksDB is an SSD optimized version of LevelDB, which was designed specifically for rotating disks. As such RocksDB's design isn't aimed at SSDs.

3 SSI: Serializable Snapshot Isolation. For more details, see the blog post Concurrent ACID Transactions in Badger

4 Badger provides direct access to value versions via its Iterator API. Users can also specify how many versions to keep per key via Options.

Benchmarks

We have run comprehensive benchmarks against RocksDB, Bolt and LMDB. The benchmarking code, and the detailed logs for the benchmarks can be found in the badger-bench repo. More explanation, including graphs can be found the blog posts (linked above).

Other Projects Using Badger

Below is a list of known projects that use Badger:

  • 0-stor - Single device object store.
  • Dgraph - Distributed graph database.
  • Jaeger - Distributed tracing platform.
  • TalariaDB - Distributed, low latency time-series database.
  • Dispatch Protocol - Blockchain protocol for distributed application data analytics.
  • Sandglass - distributed, horizontally scalable, persistent, time sorted message queue.
  • Usenet Express - Serving over 300TB of data with Badger.
  • go-ipfs - Go client for the InterPlanetary File System (IPFS), a new hypermedia distribution protocol.
  • gorush - A push notification server written in Go.
  • emitter - Scalable, low latency, distributed pub/sub broker with message storage, uses MQTT, gossip and badger.
  • GarageMQ - AMQP server written in Go.
  • RedixDB - A real-time persistent key-value store with the same redis protocol.
  • BBVA - Raft backend implementation using BadgerDB for Hashicorp raft.
  • Riot - An open-source, distributed search engine.
  • Fantom - aBFT Consensus platform for distributed applications.
  • decred - An open, progressive, and self-funding cryptocurrency with a system of community-based governance integrated into its blockchain.
  • OpenNetSys - Create useful dApps in any software language.
  • HoneyTrap - An extensible and opensource system for running, monitoring and managing honeypots.
  • Insolar - Enterprise-ready blockchain platform.
  • IoTeX - The next generation of the decentralized network for IoT powered by scalability- and privacy-centric blockchains.
  • go-sessions - The sessions manager for Go net/http and fasthttp.
  • Babble - BFT Consensus platform for distributed applications.
  • Tormenta - Embedded object-persistence layer / simple JSON database for Go projects.
  • BadgerHold - An embeddable NoSQL store for querying Go types built on Badger
  • Goblero - Pure Go embedded persistent job queue backed by BadgerDB
  • Surfline - Serving global wave and weather forecast data with Badger.
  • Cete - Simple and highly available distributed key-value store built on Badger. Makes it easy bringing up a cluster of Badger with Raft consensus algorithm by hashicorp/raft.
  • Volument - A new take on website analytics backed by Badger.
  • Sloop - Kubernetes History Visualization.
  • KVdb - Hosted key-value store and serverless platform built on top of Badger.

If you are using Badger in a project please send a pull request to add it to the list.

Frequently Asked Questions

My writes are getting stuck. Why?

Update: With the new Value(func(v []byte)) API, this deadlock can no longer happen.

The following is true for users on Badger v1.x.

This can happen if a long running iteration with Prefetch is set to false, but a Item::Value call is made internally in the loop. That causes Badger to acquire read locks over the value log files to avoid value log GC removing the file from underneath. As a side effect, this also blocks a new value log GC file from being created, when the value log file boundary is hit.

Please see Github issues #293 and #315.

There are multiple workarounds during iteration:

  1. Use Item::ValueCopy instead of Item::Value when retrieving value.
  2. Set Prefetch to true. Badger would then copy over the value and release the file lock immediately.
  3. When Prefetch is false, don't call Item::Value and do a pure key-only iteration. This might be useful if you just want to delete a lot of keys.
  4. Do the writes in a separate transaction after the reads.

My writes are really slow. Why?

Are you creating a new transaction for every single key update, and waiting for it to Commit fully before creating a new one? This will lead to very low throughput.

We have created WriteBatch API which provides a way to batch up many updates into a single transaction and Commit that transaction using callbacks to avoid blocking. This amortizes the cost of a transaction really well, and provides the most efficient way to do bulk writes.

wb := db.NewWriteBatch()
defer wb.Cancel()

for i := 0; i < N; i++ {
  err := wb.Set(key(i), value(i), 0) // Will create txns as needed.
  handle(err)
}
handle(wb.Flush()) // Wait for all txns to finish.

Note that WriteBatch API does not allow any reads. For read-modify-write workloads, you should be using the Transaction API.

I don't see any disk writes. Why?

If you're using Badger with SyncWrites=false, then your writes might not be written to value log and won't get synced to disk immediately. Writes to LSM tree are done inmemory first, before they get compacted to disk. The compaction would only happen once MaxTableSize has been reached. So, if you're doing a few writes and then checking, you might not see anything on disk. Once you Close the database, you'll see these writes on disk.

Reverse iteration doesn't give me the right results.

Just like forward iteration goes to the first key which is equal or greater than the SEEK key, reverse iteration goes to the first key which is equal or lesser than the SEEK key. Therefore, SEEK key would not be part of the results. You can typically add a 0xff byte as a suffix to the SEEK key to include it in the results. See the following issues: #436 and #347.

Which instances should I use for Badger?

We recommend using instances which provide local SSD storage, without any limit on the maximum IOPS. In AWS, these are storage optimized instances like i3. They provide local SSDs which clock 100K IOPS over 4KB blocks easily.

I'm getting a closed channel error. Why?

panic: close of closed channel
panic: send on closed channel

If you're seeing panics like above, this would be because you're operating on a closed DB. This can happen, if you call Close() before sending a write, or multiple times. You should ensure that you only call Close() once, and all your read/write operations finish before closing.

Are there any Go specific settings that I should use?

We highly recommend setting a high number for GOMAXPROCS, which allows Go to observe the full IOPS throughput provided by modern SSDs. In Dgraph, we have set it to 128. For more details, see this thread.

Are there any Linux specific settings that I should use?

We recommend setting max file descriptors to a high number depending upon the expected size of your data. On Linux and Mac, you can check the file descriptor limit with ulimit -n -H for the hard limit and ulimit -n -S for the soft limit. A soft limit of 65535 is a good lower bound. You can adjust the limit as needed.

I see "manifest has unsupported version: X (we support Y)" error.

This error means you have a badger directory which was created by an older version of badger and you're trying to open in a newer version of badger. The underlying data format can change across badger versions and users will have to migrate their data directory. Badger data can be migrated from version X of badger to version Y of badger by following the steps listed below. Assume you were on badger v1.6.0 and you wish to migrate to v2.0.0 version.

  1. Install badger version v1.6.0

    • cd $GOPATH/src/github.com/dgraph-io/badger

    • git checkout v1.6.0

    • cd badger && go install

      This should install the old badger binary in your $GOBIN.

  2. Create Backup
    • badger backup --dir path/to/badger/directory -f badger.backup
  3. Install badger version v2.0.0

    • cd $GOPATH/src/github.com/dgraph-io/badger

    • git checkout v2.0.0

    • cd badger && go install

      This should install new badger binary in your $GOBIN

  4. Install badger version v2.0.0

    • badger restore --dir path/to/new/badger/directory -f badger.backup

      This will create a new directory on path/to/new/badger/directory and add badger data in newer format to it.

NOTE - The above steps shouldn't cause any data loss but please ensure the new data is valid before deleting the old badger directory.

Why do I need gcc to build badger? Does badger need CGO?

Badger does not directly use CGO but it relies on https://github.com/DataDog/zstd library for zstd compression and the library requires gcc/cgo. You can build badger without cgo by running CGO_ENABLED=0 go build. This will build badger without the support for ZSTD compression algorithm.

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