Note on upgrading to 2.0

If you are using LevelDB in a 1.x version of Riak, are upgrading to 2.0, and wish to keep using your old app.config file for configuration, make sure to follow the steps for setting the total_leveldb_mem_percent parameter in the 2.0 upgrade guide.

eLevelDB is an Erlang application that encapsulates LevelDB, an open-source, on-disk key/value store created by Google Fellows Jeffrey Dean and Sanjay Ghemawat.

LevelDB is a relatively new entrant into the growing list of key/value database libraries, but it has some very interesting qualities that we believe make it an ideal candidate for use in Riak. LevelDB’s storage architecture is more like BigTable’s memtable/sstable model than it is like Bitcask. This design and implementation provide the possibility of a storage engine without Bitcask’s RAM limitation.

Note: Riak uses a fork of LevelDB. The code can be found on Github.

A number of changes have been introduced in the LevelDB backend in Riak 2.0:

  • There is now only one performance-related setting that Riak users need to define—leveldb.total_mem_percent—as LevelDB now dynamically sizes the file cache and block sizes based upon active vnodes assigned to the node.
  • The LevelDB backend in Riak 2.0 utilizes a new, faster threading model for background compaction work on .sst table files. The new model has increased throughput by at least 10% in all test scenarios.
  • Delete operations now receive priority handling in compaction selection, which means more aggressive reclaiming of disk space than in previous versions of Riak’s LevelDB backend.
  • Nodes storing massive key datasets (e.g. in the billions of keys) now receive increased throughput due to automatic management of LevelDB’s block size parameter. This parameter is slowly raised to increase the number of files that can open simultaneously, improving random read performance.


  1. License — The LevelDB and eLevelDB licenses are the New BSD License and the Apache 2.0 License, respectively. We’d like to thank Google and the authors of LevelDB at Google for choosing a completely FLOSS license so that everyone can benefit from this innovative storage engine.
  2. Data compression — LevelDB provides two compression algorithms to reduce storage size and increase efficient use of storage bandwidth:

    • Google’s Snappy data compression
    • LZ4 data compression

    Enabling compression means more CPU usage but less disk space. Compression is especially good for text data, including raw text, Base64, JSON, etc.


  1. Read access can be slow when there are many levels to search
  2. LevelDB may have to do a few disk seeks to satisfy a read; one disk seek per level and, if 10% of the database fits in memory, one seek for the last level (since all of the earlier levels should end up cached in the OS buffer cache for most filesystems) whereas if 1% fits in memory, LevelDB will need two seeks.

Installing eLevelDB

Riak ships with eLevelDB included within the distribution, so there is no separate installation required. However, Riak is configured to use the Bitcask storage engine by default. To switch to eLevelDB, set the storage_backend variable in riak.conf to leveldb:

storage_backend = leveldb
{riak_kv, [
    %% ...
    {storage_backend, riak_kv_eleveldb_backend},
    %% ...

Configuring eLevelDB

eLevelDb’s default behavior can be modified by adding/changing parameters in the eleveldb section of the riak.conf. The section below details the parameters you’ll use to modify eLevelDB.

The configuration values that can be set in your riak.conf for eLevelDB are as follows:

Config Description Default
leveldb.data_root LevelDB data root ./data/leveldb
leveldb.maximum_memory.percent Defines the percentage (between 1 and 100) of total server memory to assign to LevelDB. LevelDB will dynamically adjust its internal cache sizes as Riak activates/inactivates vnodes on this server to stay within this size. 70

If you are using the older, app.config-based system, the equivalent to the leveldb.data_root is the data_root setting, as in the following example:

{eleveldb, [
    {data_root, "/path/to/leveldb"},

    %% Other eleveldb-specific settings

The leveldb.maximum_memory.percent setting is only available in the newer configuration system.

Below are general configuration recommendations for Linux distributions. Individual users may need to tailor these settings for their application.


For production environments, please see System Performance Tuning for the recommended /etc/sysctl.conf settings.

Block Device Scheduler

Beginning with the 2.6 kernel, Linux gives you a choice of four I/O elevator models. We recommend using the NOOP elevator. You can do this by changing the scheduler on the Linux boot line: elevator=noop.

ext4 Options

The ext4 filesystem defaults include two options that increase integrity but slow performance. Because Riak’s integrity is based on multiple nodes holding the same data, these two options can be changed to boost LevelDB’s performance. We recommend setting: barrier=0 and data=writeback.

CPU Throttling

If CPU throttling is enabled, disabling it can boost LevelDB performance in some cases.

No Entropy

If you are using https protocol, the 2.6 kernel is widely known for stalling programs waiting for SSL entropy bits. If you are using https, we recommend installing the HAVEGE package for pseudorandom number generation.


We recommend setting clocksource=hpet on your Linux kernel’s boot line. The TSC clocksource has been identified to cause issues on machines with multiple physical processors and/or CPU throttling.


We recommend setting vm.swappiness=0 in /etc/sysctl.conf. The vm.swappiness default is 60, which is aimed toward laptop users with application windows. This was a key change for MySQL servers and is often referenced in database performance literature.

Implementation Details

LevelDB is a Google-sponsored open source project that has been incorporated into an Erlang application and integrated into Riak for storage of key/value information on disk. The implementation of LevelDB is similar in spirit to the representation of a single Bigtable tablet (section 5.3).

How Levels Are Managed

LevelDB is a memtable/sstable design. The set of sorted tables is organized into a sequence of levels. Each level stores approximately ten times as much data as the level before it. The sorted table generated from a flush is placed in a special young level (also called level-0). When the number of young files exceeds a certain threshold (currently four), all of the young files are merged together with all of the overlapping level-1 files to produce a sequence of new level-1 files (a new level-1 file is created for every 2MB of data.)

Files in the young level may contain overlapping keys. However files in other levels have distinct non-overlapping key ranges. Consider level number L where L >= 1. When the combined size of files in level-L exceeds (10^L) MB (i.e. 10MB for level-1, 100MB for level-2, …), one file in level-L, and all of the overlapping files in level-(L+1) are merged to form a set of new files for level-(L+1). These merges have the effect of gradually migrating new updates from the young level to the largest level using only bulk reads and writes (i.e., minimizing expensive disk seeks).

When the size of level L exceeds its limit, LevelDB will compact it in a background thread. The compaction picks a file from level L and all overlapping files from the next level L+1. Note that if a level-L file overlaps only part of a level-(L+1) file, the entire file at level-(L+1) is used as an input to the compaction and will be discarded after the compaction. Compactions from level-0 to level-1 are treated specially because level-0 is special (files in it may overlap each other). A level-0 compaction may pick more than one level-0 file in case some of these files overlap each other.

A compaction merges the contents of the picked files to produce a sequence of level-(L+1) files. LevelDB will switch to producing a new level-(L+1) file after the current output file has reached the target file size (2MB). LevelDB will also switch to a new output file when the key range of the current output file has grown enough to overlap more then ten level-(L+2) files. This last rule ensures that a later compaction of a level-(L+1) file will not pick up too much data from level-(L+2).

Compactions for a particular level rotate through the key space. In more detail, for each level L, LevelDB remembers the ending key of the last compaction at level L. The next compaction for level L will pick the first file that starts after this key (wrapping around to the beginning of the key space if there is no such file).

Level-0 compactions will read up to four 1MB files from level-0, and at worst all the level-1 files (10MB) (i.e., LevelDB will read 14MB and write 14MB in that case).

Other than the special level-0 compactions, LevelDB will pick one 2MB file from level L. In the worst case, this will overlap with approximately 12 files from level L+1 (10 because level-(L+1) is ten times the size of level-L, and another two at the boundaries since the file ranges at level-L will usually not be aligned with the file ranges at level-L+1). The compaction will therefore read 26MB, write 26MB. Assuming a disk IO rate of 100MB/s, the worst compaction cost will be approximately 0.5 second.

If we throttle the background writing to a reasonably slow rate, for instance 10% of the full 100MB/s speed, a compaction may take up to 5 seconds. If the user is writing at 10MB/s, LevelDB might build up lots of level-0 files (~50 to hold the 5*10MB). This may significantly increase the cost of reads due to the overhead of merging more files together on every read.


Levels are compacted into ordered data files over time. Compaction first computes a score for each level as the ratio of bytes in that level to desired bytes. For level 0, it computes files / desired files instead. The level with the highest score is compacted.

When compacting L0 the only special case to consider is that after picking the primary L0 file to compact, it will check other L0 files to determine the degree to which they overlap. This is an attempt to avoid some I/O, we can expect L0 compactions to usually if not always be “all L0 files”.

See the PickCompaction routine in 1 for all the details.

Comparison of eLevelDB and Bitcask

LevelDB is a persistent ordered map; Bitcask is a persistent hash table (no ordered iteration). Bitcask stores keys in memory, so for databases with large number of keys it may exhaust available physical memory and then swap into virtual memory causing a severe slow down in performance. Bitcask guarantees at most one disk seek per look-up. LevelDB may have to do a small number of disk seeks. For instance, a read needs one disk seek per level. If 10% of the database fits in memory, LevelDB will need to do one seek (for the last level since all of the earlier levels should end up cached in the OS buffer cache). If 1% fits in memory, LevelDB will need two seeks.


LevelDB never writes in place: it always appends to a log file, or merges existing files together to produce new ones. So an OS crash will cause a partially written log record (or a few partially written log records). LevelDB recovery code uses checksums to detect this and will skip the incomplete records.

eLevelDB Database Files

Below are two directory listings showing what you would expect to find on disk when using eLevelDB. In this example, we use a 64-partition ring which results in 64 separate directories, each with their own LevelDB database:

|-- 0
|   |-- 000003.log
|   |-- CURRENT
|   |-- LOCK
|   |-- LOG
|   `-- MANIFEST-000002
|-- 1004782375664995756265033322492444576013453623296
|   |-- 000005.log
|   |-- CURRENT
|   |-- LOCK
|   |-- LOG
|   |-- LOG.old
|   `-- MANIFEST-000004
|-- 1027618338748291114361965898003636498195577569280
|   |-- 000005.log
|   |-- CURRENT
|   |-- LOCK
|   |-- LOG
|   |-- LOG.old
|   `-- MANIFEST-000004

... etc ...

`-- 981946412581700398168100746981252653831329677312
    |-- 000005.log
    |-- CURRENT
    |-- LOCK
    |-- LOG
    |-- LOG.old
    `-- MANIFEST-000004

64 directories, 378 files

After performing a large number of PUT (write) operations, the Riak cluster running eLevelDB will look something like this:

tree leveldb

The result should look something like this:

├── 0
│   ├── 000003.log
│   ├── CURRENT
│   ├── LOCK
│   ├── LOG
│   ├── MANIFEST-000002
│   ├── sst_0
│   ├── sst_1
│   ├── sst_2
│   ├── sst_3
│   ├── sst_4
│   ├── sst_5
│   └── sst_6
├── 1004782375664995756265033322492444576013453623296
│   ├── 000003.log
│   ├── CURRENT
│   ├── LOCK
│   ├── LOG
│   ├── MANIFEST-000002
│   ├── sst_0
│   ├── sst_1
│   ├── sst_2
│   ├── sst_3
│   ├── sst_4
│   ├── sst_5
│   └── sst_6

... etc ...

Tiered Storage

Google’s original LevelDB implemented stored all .sst table files in a single database directory. In Riak 1.3, the original LevelDB code was modified to store .sst files in subdirectories representing each “level” of the file, e.g. sst_0 or sst_1, in the name of speeding up database repair operations.

An additional advantage of this approach is that it enables Riak operators to mount alternative storage devices at each level of a LevelDB database. This can be an effective strategy because LevelDB is write intensive in lower levels, with the write intensity declining as the level number increases. This is due to LevelDB’s storage strategy, which places more frequently updated data in lower levels.

Because write intensity differs by level, performance can be improved by mounting faster, more expensive storage arrays in lower levels and slower, less expensive arrays at higher levels. Tiered storage enables you to configure the level at which LevelDB switches from a faster array to a slower array.

Note on write throttling

High-volume, sustained write operations can occasionally fill the higher-speed storage arrays before LevelDB has had the opportunity to move data to the low-speed arrays. LevelDB’s write throttle will slow incoming write operations to allow compactions to catch up, as would be the case when using a single storage array.

Configuring Tiered Storage

If you are using the newer, riak.conf-based configuration system, the following parameters can be used to configure LevelDB tiered storage:

Parameter Description
leveldb.tiered The level number at which data should switch to the slower array. The default is 0, which disables the feature.
leveldb.tiered.path.fast The path prefix for .sst files below the level set by leveldb.tiered
leveldb.tiered.path.slow The path prefix for .sst files at and above the level set by leveldb.tiered

If you are using the older, app.config-based system, the example below will show you the equivalents of the settings listed in the table above.


The following example LevelDB tiered storage configuration for Riak 2.0 sets the level for switching storage arrays to 4 and the file path prefix to fast_raid for the faster array and slow_raid for the slower array:

leveldb.tiered = 4
leveldb.tiered.path.fast = /mnt/fast_raid
leveldb.tiered.path.slow = /mnt/slow_raid
{eleveldb, [
    {tiered_slow_level, 4},
    {tiered_fast_prefix, "/mnt/fast_raid"},
    {tiered_slow_prefix, "/mnt/slow_raid"}

With this configuration, level directories sst_0 through sst_3 will be stored in /mnt/fast_raid, while directories sst_4 and sst_6 will be stored in /mnt/slow_raid.

Selecting a Level

LevelDB will perform optimally when as much data as possible is stored in the faster array. The amount of data that can be stored in the faster array depends on the size of your array and the total number of LevelDB databases (i.e. the total number of Riak vnodes) in your cluster. The following table shows approximate sizes (in megabytes) for each of the following sizes: the amount of raw data stored in the level, the cumulative size of all levels up to the specified level, and the cumulative size including active anti-entropy data.

Level Level Size Cumulative Size Cumulative with AAE
0 360 360 720
1 2,160 2,520 5,040
2 2,940 5,460 10,920
3 6,144 11,604 23,208
4 122,880 134,484 268,968
5 2,362,232 2,496,716 4,993,432
6 not limited not limited not limited

To select the appropriate value for leveldb.tiered, use the following steps:

  • Determine the value of (ring size) / (N - 1), where ring size is the value of the ring_size configuration parameter and N is the number of nodes in the cluster. For a ring_size of 128 and a cluster with 10 nodes, the value would be 14.
  • Select either the Cumulative Size or Cumulative with AAE column from the table above. Select the third column if you are not using active anti-entropy or the fourth column if you are (i.e. if the anti_entropy configuration parameter is set to active).
  • Multiply the value from the first step by the cumulative column in each row in the table. The first result that exceeds your fast storage array capacity will provide the level number that should be used for your leveldb.tiered setting.

Migrating from One Configuration to Another

If you want to use tiered storage in a new Riak installation, you don’t need to take any steps beyond setting configuration. The rest is automated.

But if you’d like to use tiered storage in an existing installation that is not currently using it, you will need to manually move your installation’s .sst files from one configuration to another.