To be able to answer full-text search queries fast, Manticore needs to build a special data structure optimized for such queries from your text data. This structure is called index; and the process of building index from text is called indexing.
An index identifier must be a single word, that can contain letters, numbers and underscores. It must start with a letter.
Different index types are well suited for different tasks. For example, a disk-based tree-based index would be easy to update (ie. insert new documents to existing index), but rather slow to search. Manticore architecture allows internally for different index types, or backends, to be implemented comparatively easily.
Manticore provides 2 different backends: a disk index backend, and a RT (realtime) index backend.
Disk indexes are designed to provide maximum indexing and searching speed, while keeping the RAM footprint as low as possible. That comes at a cost of text index updates. You can not update an existing document or incrementally add a new document to a disk index. You only can batch rebuild the entire disk index from scratch. (Note that you still can update document’s attributes on the fly, even with the disk indexes.)
This “rebuild only” limitation might look as a big constraint at a first glance. But in reality, it can very frequently be worked around rather easily by setting up multiple disk indexes, searching through them all, and only rebuilding the one with a fraction of the most recently changed data. See Live index updates for details.
RT indexes enable you to implement dynamic updates and incremental additions to the full text index. RT stands for Real Time and they are indeed “soft realtime” in terms of writes, meaning that most index changes become available for searching as quick as 1 millisecond or less, but could occasionally stall for seconds. (Searches will still work even during that occasional writing stall.) Refer to Real-time indexes for details.
Manticore supports so-called distributed indexes. Compared to disk and RT indexes, those are not a real physical backend, but rather just lists of either local or remote indexes that can be searched transparently to the application, with Manticore doing all the chores of sending search requests to remote machines in the cluster, aggregating the result sets, retrying the failed requests, and even doing some load balancing. See Distributed searching for a discussion of distributed indexes.
Template indexes are indexes with no storage backend. They can be used operations that involve only data from input, like keywords and snippets generation.
Percolate indexes are special Real-Time indexes that store queries instead of documents. They are used for prospective searches ( or “search in reverse”). Refer to Percolate query for more details.
There can be as many indexes per configuration file as necessary.
indexer utility can reindex either all of them (if
is specified), or a certain explicitly specified subset.
utility will serve all the specified indexes, and the clients can
specify what indexes to search in run time.
Each index consists of a number of files.
Small index components are fully loaded into memory. Big index components are read from disk as needed. Currently these use seek+read or mmap() to retrieve data from disk. Attribute components are opened and mapped with mmap(). They can be loaded fully in memory or left on disk and read when needed.
Plain indexes and RealTime indexes chunks:
|spa||scalar attrs||mmap(), also see access_plain_attrs|
|spd||document lists||read from disk and may be cached by OS or mmaped, also see access_doclists|
|spi||dictionary||always loaded in memory|
|sph||index/chunk header||always loaded in memory|
|spk||kill list||loaded and discarded |
|spl||index lock file||on disk only|
|sphi||secondary index histograms||always loaded in memory|
|spp||keyword positions||read from disk and may be cached by OS or mmaped, also see access_hitlists|
|spb||var-length attrs||mmap(), also see access_blob_attrs|
 Kill lists - loaded in memory at startup and discarded after they are applied to targets
RealTime indexes also have:
|kill||RT kill ||on disk only|
|meta||RT header||always loaded in memory|
|lock||RT lock file||on disk only|
|ram||RAM chunk copy ||on disk only|
 RT kill - documents that are REPLACEd get cleared when the RAM chunk is dumped as a disk chunk.
 RAM chunk copy - created when the RAM chunk is flushed to disk. Cleared when the RAM chunk is dumped as a disk chunk.
Accessing index files¶
The daemon uses two access modes to read index data - seek+read and mmap.
In seek+read mode the daemon performs system call pread(2) to read document
lists and keyword positions, i.e.
spp files. Internal read buffers are
used to optimize reading. The size of these buffers can be tuned with options read_buffer_docs
and read_buffer_hits. There is also preopen option that allows to control
the amount of files opened by daemon at start.
In mmap access mode the search daemon just maps index’s file into memory with
mmap(2) system call and OS caches file content by itself. Options
read_buffer_docs and read_buffer_hits have no effect for corresponding
files in this mode. This reader could be used for scalar (int, float, boolean, timestamp)
attributes, var-length (string, mva, json) attributes, document lists and keyword
mmap reader can also lock index’s data in memory via mlock(2) privileged call which prevents swapping out
of the cached data to disk by OS.
filedaemon reads index file from disk with seek+read using internal buffers on file access
mmapdaemon maps index file into memory and OS caches up its contents on file access
mmap_prereaddaemon maps index file into memory and a background thread reads it once to warm up the cache
mlockdaemon maps index file into memory and then issues mlock system call to cache up the file contents and lock it into memory to prevent it being swapped out
Here is a table which can help you select your desired mode:
|index part||keep it on disk||keep it in memory||cached in memory on daemon start||lock it in memory|
|.spa (plain attributes)||access_plain_attrs=mmap - the file will mapped to RAM, but your OS will decide whether to really load it to RAM or not and can easily swap it out (default)||access_plain_attrs = mmap_preread (default)||access_plain_attrs = mlock|
|.spb (blob attributes)||access_blob_attrs=mmap - the file will mapped to RAM, but your OS will decide whether to really load it to RAM or not and can easily swap it out (default)||access_blob_attrs = mmap_preread (default)||access_blob_attrs = mlock|
|.spd (doc lists)||access_doclists = file (default)||access_doclists = mmap, may be still swapped out by OS||no||no|
|.spp (hit lists)||access_hitlists = file (default)||access_hitlists = mmap, may be still swapped out by OS||no||no|
There’s also a searchd command line option
--force-preread that instructs the
daemon to wait until the attribute files are read prior to starting accepting incoming connections.
Starting daemon with this option allows to make sure that all mapped index files will be ‘warmed’
by touching every page of the maps. OS, in turn, usually tries to keep all ‘warm’ data in the disk cache,
providing maximum performance.
The recommendations are:
- If search performance is very important and you have enough memory - use mlock for attributes and mmap for doclists/hitlists. Be aware mlock is a privileged system call and the user running searchd should have enough privileges. Read here for details
- If you can’t afford lower performance on start and ready to wait longer on start until it’s warmed up - use –force-preread
- If you want searchd to be able to restart faster - stay with mmap_preread
- If you want to save RAM - do not use mlock, then your OS will decide what should be in memory at any given moment of time depending on what is read from disk more frequently
- If search performance doesn’t matter at all and you want to save maximum RAM - use access_doclists/access_hitlists=file and access_plain_attrs/access_blob_attrs=mmap
The default mode is to mmap and pre-read attributes and access doclists/hitlists directly from disk which provides decent search performance, optimal memory usage and faster searchd restart in most cases.
Operations on indexes¶
Plain indexes can only be created by indexer tool. If a plain index is only declared in configuration,but not created, the daemon will print a warning about that. It must be also noted that the daemon requires at least one index of type RT, percolate or plain in order to start.
Real-Time, percolate and template indexes can be declared in the configuration and they will be created (with empty data) at daemon start.
Loading or discarding indexes¶
At startup, daemon will try to load and make available all indexes found in the configuration file.
HUP signal can be used to make the daemon reload the configuration. This way new indexes can be loaded or existing indexes can be discarded while the daemon is running. Changing the type of an index, for example from template to Real-Time, can also be performed during a configuration reload.
Alternative to signaling HUP to searchd daemon, the RELOAD INDEXES SphinxQL command can be used.
Refreshing a plain index already loaded by daemon requires running indexer with –rotate parameter. In this case, a new version of the plain index is created and when ready, a HUP is send to daemon, which will load the new version of the index in the memory and discard the old one.
Index schema can be changed on-the-fly in case of attribute. Full-text fields however require re-creating the index.
Change of tokenization settings requires a remaking in case of plain indexes. For Real-Time indexes, these can be made on-the-fly using ALTER RECONFIGURE but they will affect only new content added to index, as it’s not possible yet to re-tokenize already indexed texts.
Some settings like access_plain_attrs, which don’t alter in any way the index, don’t require an index rebuild, just a reload.