Data types

Full-text fields and attributes

Manticore's data types can be split into full-text fields and attributes.

Full-text fields are indexed and can be searched for keywords. They cannot be used in filtering, sorting or grouping. However, original document content can be retrieved and used in result set highlighting.

Full-text fields are represented by the Text data type. All the other data types are attributes.

Attributes are additional values associated with each document that can be used to perform additional filtering and sorting during search.

It is often desired to additionally process full-text search results based not only on matching document ID and its rank, but on a number of other per-document values as well. For instance, one might need to sort news search results by date and then relevance, or search through products within specified price range, or limit blog search to posts made by selected users, or group results by month. To do that efficiently, Manticore allows to attach a number of additional attributes to each document. It's then possible to use stored values to filter, sort, or group full-text matches.

Attributes, unlike the fields, are not full-text indexed. They are stored in the index, but it is not possible to search them as full-text.

A good example for attributes would be a forum posts index. Assume that only title and content fields need to be full-text searchable - but that sometimes it is also required to limit search to a certain author or a sub-forum (ie. search only those rows that have some specific values of author_id or forum_id); or to sort matches by post_date column; or to group matching posts by month of the post_date and calculate per-group match counts.

📋
create table forum(title text, content text, author_id int, forum_id int, post_date timestamp);

This example shows running a full-text query filtered by author_id, forum_id and sorted by post_date.

📋
select * from forum where author_id=123 and forum_id in (1,3,7) order by post_date desc

Below is the list of data types supported by Manticore Search:

Document ID

The identifier of a document in the index. Document IDs must be unique signed positive non-zero 64-bit integers. Note that no negative or zero values are allowed. Document IDs are implicit and have no declaration. Update operation is forbidden on document ids.

Text

Text data type forms the full-text part of the index. Text fields are indexed and can be searched for keywords.

Text is passed through an analyzer pipeline that converts the text to words, applies morphology transformations etc. Eventually a full-text index (a special data structure that enables quick searches for a keyword) gets built from that text.

Full-text fields can only be used in MATCH() clause and cannot be used for sorting or aggregation. Words are stored in an inverted index along with references to the fields they belong and positions in the field. This allows to search a word inside each field and to use advanced operators like proximity. By default the original text of the fields is both indexed and stored in document storage. It means that the original text can be returned with the query results and it can be used in search result highlighting.

📋
create table products(title text);

This behavior can overridden by explicitly specifying that the text is only indexed.

📋
create table products(title text indexed);

Fields are named, and you can limit your searches to a single field (e.g. search through "title" only) or a subset of fields (eg. to "title" and "abstract" only). Manticore index format generally supports up to 256 fields.

📋
select * from products where match('@title first');

String

Unlike full-text fields, string attributes are stored as they are received and cannot be used in full-text searches. Instead they are returned in results, they can be used in WHERE clause for comparison filtering or REGEX and they can be used for sorting and aggregation. In general it's not recommended to store large texts in string attributes, but use string attributes for metadata like names, titles, tags, keys.

📋
create table products(title text, keys string);
MORE

Integer

Integer type allows storing 32 bit unsigned integer values.

📋
create table products(title text, price int);

Integers can be stored in shorter sizes than 32 bit by specifying a bit count. For example if we want to store a numeric value which we know is not going to be bigger than 8, the type can be defined as bit(3). Bitcount integers perform slower than the full size ones, but they require less RAM. They are saved in 32-bit chunks, so in order to save space they should be grouped at the end of attributes definitions (otherwise a bitcount integer between 2 full-size integers will occupy 32 bits as well).

📋
create table products(title text, flags bit(3), tags bit(2) );

Big Integer

Big integers are 64-bit wide signed integers.

📋
create table products(title text, price bigint );

Timestamps

Timestamp type represents unix timestamps which is stored as a 32-bit integer. The difference is that time and date functions are available for the timestamp type.

📋
create table products(title text, date timestamp);

Float

Real numbers are stored as 32-bit IEEE 754 single precision floats.

📋
create table products(title text, coeff float);

Unlike integer types, equal comparison of floats is forbidden due to rounding errors. A near equal can be used instead, by checking the absolute error margin.

📋
select abs(a-b)<=0.00001 from products

Another alternative, which can also be used to perform IN(attr,val1,val2,val3) is to compare floats as integers by choosing a multiplier factor and convert the floats to integers in operations. Example illustrates modifying IN(attr,2.0,2.5,3.5) to work with integer values.

📋
select in(ceil(attr*100),200,250,350) from products

JSON

This data type allows storing JSON objects for schema-less data.

📋
create table products(title text, data json);

JSON properties can be used in most operations. There are also special functions such as ALL(), ANY(), GREATEST(), LEAST() and INDEXOF() that allow traversal of property arrays.

📋
select indexof(x>2 for x in data.intarray) from products

Text properties are treated same as strings so it's not possible to use them in full-text matches expressions, but string functions like REGEX() can be used.

📋
select regex(data.name, 'est') as c from products where c>0

In case of JSON properties, enforcing data type is required to be casted in some situations for proper functionality. For example in case of float values DOUBLE() must be used for proper sorting.

📋
select * from products order by double(data.myfloat) desc

Multi-value integer (MVA)

Multi-value attributes allow storing variable-length lists of 32-bit unsigned integers. It can be used to store one-to-many numeric values like tags, product categories, properties.

📋
create table products(title text, product_codes multi);

It supports filtering and aggregation, but not sorting. Filtering can made of condition that requires at least one element to pass (using ANY()) or all (ALL()).

📋
select * from products where any(product_codes)=3

Information like least or greatest element and length of the list can be extracted. An example shows ordering by the least element of a multi-value attribute.

📋
select least(product_codes) l from products order by l asc

When grouping by multi-value attribute, a document will contribute to as many groups as there are different values associated with that document. For instance, if the collection contains exactly 1 document having a 'product_codes' multi-value attribute with values 5, 7, and 11, grouping on 'product_codes' will produce 3 groups with COUNT(*)equal to 1 and GROUPBY() key values of 5, 7, and 11 respectively. Also note that grouping by multi-value attributes might lead to duplicate documents in the result set because each document can participate in many groups.

SQL
📋
insert into products values ( 1, 'doc one', (5,7,11) );
select id, count(*), groupby() from products group by product_codes;
Response
Query OK, 1 row affected (0.00 sec)

+------+----------+-----------+
| id   | count(*) | groupby() |
+------+----------+-----------+
|    1 |        1 |        11 |
|    1 |        1 |         7 |
|    1 |        1 |         5 |
+------+----------+-----------+
3 rows in set (0.00 sec)

The order of the numbers inserted as values of multi-valued attributes is not preserved. Values are stored internally as a sorted set.

📋
insert into product values (1,'first',(4,2,1,3));
select * from products;
Response
Query OK, 1 row affected (0.00 sec)

+------+---------------+-------+
| id   | product_codes | title |
+------+---------------+-------+
|    1 | 1,2,3,4       | first |
+------+---------------+-------+
1 row in set (0.01 sec)

Multi-value big integer

A data type type that allows storing variable-length lists of 64-bit signed integers. It has the same functionality as multi-value integer.

📋
create table products(title text, values multi64);

Creating a local index

There are 2 different approaches to deal with indexes in Manticore:

Online schema management (RT mode)

Real-time mode requires no index definition in the configuration file and data_dir directive in searchd section. Index files are stored inside the data_dir.

Replication is available only in this mode.

In this mode you can use SQL commands like CREATE TABLE, ALTER TABLE and DROP TABLE to create and change index schema and drop it. This mode is especially useful for real-time and percolate indexes.

Index names are case insensitive in RT mode.

Defining index schema in config (Plain mode)

In this mode you can specify index schema in config which will be read on Manticore start and if the index doesn't exist yet it will be created. This mode is especially useful for plain indexes that are built upon indexing data from an external storage.

Dropping indexes is only possible by removing them from the configuration file or by removing the path setting and sending a HUP signal to the server or restarting it.

Index names are case sensitive in this mode.

All index types are supported in this mode.

Index types and modes

Index type RT mode plain mode
Real-time yes yes
Plain no yes
Percolate yes yes
Distributed yes yes
Template yes no

Real-time index

Real-time index is a main type of Manticore indexes. It allows adding, updating and deleting documents with immediate availability of the changes. Real-time index settings can be defined in a configuration file or online via CREATE/UPDATE/DELETE/ALTER commands.

Real-time index internally consists of one or multiple plain indexes called chunks. There can be:

  • multiple disk chunks. They are stored on disk with the same structure as any plain index
  • single ram chunk. Stored in memory and used as an accumulator of changes

RAM chunk size is controlled by rt_mem_limit. Once the limit is exceeded the RAM chunk is flushed to disk in a form of a disk chunk. When there are too many disk chunks they can be merged into one for better performance using command OPTIMIZE.

📋
create table products(title text, price float) morphology='stem_en';
Response
Query OK, 0 rows affected (0.00 sec)
Creating a real-time index via JSON over HTTP:

👍 What you can do with a real-time index:

⛔ What you cannot do with a real-time index:

  • Index data with help of indexer
  • Link it with sources for easy indexing from external storages
  • Update it's killlist_target, it's just not needed as the real-time index takes controls of it automatically

Real-time index files structure

Extension Description
.lock lock file
.ram RAM chunk
.meta RT index headers
.*.sp* disk chunks (see plain index format)