Quasar is an open source NoSQL analytics engine that can be used as a library or through a REST API to power advanced analytics across a growing range of data sources and databases, including MongoDB.
SQL² is the dialect of SQL that Quasar understands.
In the following documentation SQL² will be used interchangeably with SQL.
SQL² supports variables inside queries (SELECT * WHERE pop < :cutoff). Values for these variables, which can be any expression, should be specified as additional parameters in the url, using the variable name prefixed by var. (e.g. var.cutoff=1000). Failure to specify valid values for all variables used inside a query will result in an error. These values use the same syntax as the query itself; notably, strings should be surrounded by double quotes. Some acceptable values are 123, "CO", and DATE("2015-07-06").
Note: This requires Java 8 and Bash (Linux, Mac). Bash is not required on Windows, but the non-SBT infrastructure (e.g. the docker scripts) currently only works on Unix platforms.
The following sections explain how to build and run the various subprojects.
To compile the project and run tests, first clone the quasar repo and then execute the following command (if on Windows, reverse the slashes):
./sbt testNote: please note that we are not using here a system wide sbt, but our own copy of it (under ./sbt). This is primarily done for determinism. In order to have a reproducible build, the helper script needs to be part of the repo.
Running the full test suite can be done using docker containers for various backends:
In order to run integration tests for various backends the docker/scripts are provided to easily create dockerized backend data stores.
Of particular interest are the following two scripts:
docker/scripts/setupContainersdocker/scripts/assembleTestingConf
Quasar supports the following datastores:
quasar_mongodb_3_4_13
quasar_metastore
Knowing which backend datastores are supported you can create and configure docker containers using setupContainers. For example
if you wanted to run integration tests with mongo you would use:
./setupContainers -u quasar_metastore,quasar_mongodb_3_4_13
Note: quasar_metastore is always needed to run integration tests.
This command will pull docker images, create containers running the specified backends, and configure them appropriately for Quasar testing.
Once backends are ready we need to configure the integrations tests in order to inform Quasar about where to find the backends to test.
This information is conveyed to Quasar using the file it/testing.conf. Using the assembleTestingConf script you can generate a testing.conf
file based on the currently running containerizd backends using the following command:
./assembleTestingConf -a
After running this command your testing.conf file should look similar to this:
> cat it/testing.conf
postgresql_metastore="{\"host\":\"192.168.99.101\",\"port\":5432,\"database\":\"metastore\",\"userName\":\"postgres\",\"password\":\"\"}"
mongodb_3_4_13="mongodb://192.168.99.101:27022"
IP's will vary depending on your docker environment. In addition the scripts assume you have docker and docker-compose installed. You can find information about installing docker here.
To build a JAR for the REPL, which allows entering commands at a command-line prompt, execute the following command:
./sbt 'repl/assembly'The path of the JAR will be ./.targets/repl/scala-2.11/quasar-repl-assembly-[version].jar, where [version] is the Quasar version number.
To run the JAR, execute the following command:
java -jar [<path to jar>] [-c <config file>]As a command-line REPL user, to work with a fully functioning REPL you will need the metadata store and a mount point. See here for instructions on creating the metadata store backend using docker.
The <mountPath> specifies the path of your mount point and the remaining parameters are listed below:
| mountKey | protocol | uri |
|---|---|---|
mimir |
"<path-to-mimir-storage-directory>" | |
lwc_local |
"<path-to-mimir-storage-directory>" | |
mongodb |
mongodb:// |
MongoDB |
You will also need the metadata store. See here for getting up and running with one using docker.
By default, the REPL assembly contains only mimir and lwc_local. In order to use other mounts – such as mongodb – you will need to build the relevant backend and place the JAR in a directory where quasar can find it. This can be done in one of two ways
Create a directory where you will place individual backend JARs:
$ mkdir plugins/Now run the assembly task for the relevant backend:
$ ./sbt mongodb/assemblyThe path to the JAR will be something like ./.targets/mongodb/scala-2.11/quasar-mongodb-internal-assembly-23.1.5.jar, though the exact name of the JAR (and the directory path in question) will of course depend on the backend built (for example, mongodb/assembly will produce a very different JAR from mongodb/assembly).
For each backend that you wish to support, run that backend's assembly. See the launcher for further instructions.
This technique is designed for local development use, where the backend implementation is changing frequently. Under certain circumstances though, it may be useful for the pre-built JAR case.
As with the plugins directory approach, you will need to run the assembly task for each backend that you want to use. But instead of copying the JAR files into a directory, you will be referencing each JAR file individually using the --backend switch on the REPL JAR invocation:
java -jar [<path to jar>] [-c <config file>] --backend:quasar.physical.mongodb.MongoDb\$=.targets/mongodb/scala-2.11/quasar-mongodb-internal-assembly-23.1.5.jarReplace the JAR file in the above with the path to the backend whose assembly you ran. The --backend switch may be repeated as many times as necessary: once for each backend you wish to add. The value to the left of the = is the BackendModule object class name which defines the backend in question. Note that we need to escape the $ character which will be present in each class name, solely because of bash syntax. If you are invoking the --backend option within sbt (for example running repl/run) you do not need to escape the $.
What follows is a list of class names for each supported backend:
| mountKey | class name |
|---|---|
mongodb |
quasar.physical.mongodb.MongoDb$ |
Mimir is not included in the above, since it is already built into the core of quasar.
The value to the right of the = is a comma-separated list of paths which will be used as the classpath for the backend in question. You can include as many JARs or directories (containing classes) as you need, just as with any classpath configuration.
The various REPL JARs can be configured by using a command-line argument to indicate the location of a JSON configuration file. If no config file is specified, it is assumed to be quasar-config.json, from a standard location in the user's home directory.
The JSON configuration file must have the following format:
{
"server": {
"port": 8080
},
"metastore": {
"database": {
<metastore_config>
}
}
}Configuration for the metadata store consists of providing connection information for a supported database. Currently the H2 and PostgreSQL (9.5+) databases are supported.
To easily get up and running with a PostgreSQL metastore backend using docker see Full Testing section.
If no metastore configuration is specified, the default configuration will use an H2 database located in the default quasar configuration directory for your operating system.
An example H2 configuration would look something like
"h2": {
"location": "`database_url`"
}Where database_url can be any h2 url as described here.
A PostgreSQL configuration looks something like
"postgresql": {
"host": "localhost",
"port": 8087,
"database": "<database name>",
"userName": "<database user>",
"password": "<password for database user>",
"parameters": <an optional JSON object of parameter key:value pairs>
}The contents of the optional parameters object correspond to the various driver configuration parameters available for PostgreSQL. One example for a value of the parameters object may be a loglevel:
"parameters": {
"loglevel": 1
}Before the server can be started, the metadata store schema must be initialized. To do so utilize the "initUpdateMetaStore" command with a repl quasar jar.
If mounts are already defined in the config file, initialization will migrate those to the metadata store.
If the mount's key is "mongodb", then the connectionUri is a standard MongoDB connection string. Only the primary host is required to be present, however in most cases a database name should be specified as well. Additional hosts and options may be included as specified in the linked documentation.
For example, say a MongoDB instance is running on the default port on the same machine as Quasar, and contains databases test and students, the students database contains a collection cs101, and the connectionUri is mongodb://localhost/test. Then the filesystem will contain the paths /local/test/ and /local/students/cs101, among others.
A database can be mounted at any directory path, but database mount paths must not be nested inside each other.
To connect to MongoDB using TLS/SSL, specify ?ssl=true in the connection string, and also provide the following via system properties when launching either JAR (i.e. java -Djavax.net.ssl.trustStore=/home/quasar/ssl/certs.ts):
javax.net.ssl.trustStore: path specifying a file containing the certificate chain for verifying the server.javax.net.ssl.trustStorePassword: password for the trust store.javax.net.ssl.keyStore: path specifying a file containing the client's private key.javax.net.ssl.keyStorePassword: password for the key store.javax.net.debug: (optional) useallfor very verbose but sometimes helpful output.invalidHostNameAllowed: (optional) usetrueto disable host name checking, which is less secure but may be needed in test environments using self-signed certificates.
If the mount's key is "view" then the mount represents a "virtual" file, defined by a SQL² query. When the file's contents are read or referred to, the query is executed to generate the current result on-demand. A view can be used to create dynamic data that combines analysis and formatting of existing files without creating temporary results that need to be manually regenerated when sources are updated.
For example, given the above MongoDB mount, an additional view could be defined with a connectionUri of sql2:///?q=select%20_id%20as%20zip%2C%20city%2C%20state%20from%20%60%2Flocal%2Ftest%2Fzips%60%20where%20pop%20%3C%20%3Acutoff&var.cutoff=1000
A view can be mounted at any file path. If a view's path is nested inside the path of a database mount, it will appear alongside the other files in the database. A view will "shadow" any actual file that would otherwise be mapped to the same path. Any attempt to write data to a view will result in an error.
View mounts can optionally be cached. When cached a view is refreshed periodically in the background with respect to its associated max-age.
A cached view is created by adding the Cache-Control: max-age=<seconds> header to a /mount/fs/ request.
Like ordinary views, cached views appear as a file in the filesystem.
If the mount's key is "module" then the mount represents a "virtual" directory which contains a collection of SQL Statements. The Quasar Filesystem surfaces each SQL function definition as a file despite the fact that it is not possible to read from that file. Instead one needs to use the invoke endpoint in order to pass arguments to a particular function and get the result.
A module function can be thought of as a parameterized view, i.e. a view with "holes" that can be filled dynamically.
The value of a module mount is simply the SQL string which will be parsed into a list of SQL Statements.
To create a new module one would send a json blob similar to this one to the mount endpoint:
{ "module": "CREATE FUNCTION ARRAY_LENGTH(:foo) BEGIN COUNT(:foo[_]) END; CREATE FUNCTION USER_DATA(:user_id) BEGIN SELECT * FROM `/root/path/data/` WHERE user_id = :user_id END" }Similar to views, modules can be mounted at any directory path. If a module's path is nested inside the path of a database mount, it will appear alongside the other directory and files in the database. A module will "shadow" any actual directory that would otherwise be mapped to the same path. Any attempt to write data to a module will result in an error.
The interactive REPL accepts SQL SELECT queries.
First, choose the database to be used. Here, a MongoDB instance is mounted at
the root, and it contains a database called test:
💪 $ cd test
The "tables" in SQL queries refer to collections in the database by name:
💪 $ select * from zips where state="CO" limit 3
Mongo
db.zips.aggregate(
[
{ "$match": { "state": "CO" } },
{ "$limit": NumberLong(3) },
{ "$out": "tmp.gen_0" }],
{ "allowDiskUse": true });
db.tmp.gen_0.find();
Query time: 0.1s
city | loc[0] | loc[1] | pop | state |
---------|--------------|------------|--------|-------|
ARVADA | -105.098402 | 39.794533 | 12065 | CO |
ARVADA | -105.065549 | 39.828572 | 32980 | CO |
ARVADA | -105.11771 | 39.814066 | 33260 | CO |
💪 $ select city from zips limit 3
...
city |
----------|
AGAWAM |
CUSHMAN |
BARRE |
You may also store the result of a SQL query:
💪 $ out1 := select * from zips where state="CO" limit 3The location of a collection may be specified as an absolute path by surrounding the path with double quotes:
select * from `/test/zips`Type help for information on other commands.
Given query results like:
{"_id":"01001","city":"AGAWAM","loc":[-72.622739,42.070206],"pop":15338,"state":"MA"}
{"_id":"01002","city":"CUSHMAN","loc":[-72.51565,42.377017],"pop":36963,"state":"MA"}
{"_id":"01005","city":"BARRE","loc":[-72.108354,42.409698],"pop":4546,"state":"MA"}
{"_id":"01007","city":"BELCHERTOWN","loc":[-72.410953,42.275103],"pop":10579,"state":"MA"}
{"_id":"01008","city":"BLANDFORD","loc":[-72.936114,42.182949],"pop":1240,"state":"MA"}
{"_id":"01010","city":"BRIMFIELD","loc":[-72.188455,42.116543],"pop":3706,"state":"MA"}
{"_id":"01011","city":"CHESTER","loc":[-72.988761,42.279421],"pop":1688,"state":"MA"}
{"_id":"01012","city":"CHESTERFIELD","loc":[-72.833309,42.38167],"pop":177,"state":"MA"}
{"_id":"01013","city":"CHICOPEE","loc":[-72.607962,42.162046],"pop":23396,"state":"MA"}
{"_id":"01020","city":"CHICOPEE","loc":[-72.576142,42.176443],"pop":31495,"state":"MA"}a schema document might look like
{
"measure" : {
"kind" : "collection",
"count" : 1000.0,
"minLength" : 5.0,
"maxLength" : 5.0
},
"structure" : {
"type" : "map",
"of" : {
"city" : {
"measure" : {
"kind" : "collection",
"count" : 1000.0,
"minLength" : 3.0,
"maxLength" : 16.0
},
"structure" : {
"tag" : "_structural.string",
"type" : "array",
"of" : {
"measure" : {
"count" : 8693.0,
"distribution" : {
"state" : {
"centralMoment4" : 893992600.3364398,
"size" : 8693.0,
"centralMoment3" : -18773123.74002289,
"centralMoment2" : 876954.1582882765,
"centralMoment1" : 74.29506499482345
},
"variance" : 100.89210288636407,
"kurtosis" : 10.111128909991152,
"mean" : 74.29506499482345,
"skewness" : -2.1317240928957726
},
"min" : " ",
"max" : "Z",
"kind" : "char"
},
"structure" : {
"type" : "character"
}
}
}
},
"state" : {
"measure" : {
"kind" : "collection",
"count" : 1000.0,
"minLength" : 2.0,
"maxLength" : 2.0
},
"structure" : {
"tag" : "_structural.string",
"type" : "array",
"of" : {
"measure" : {
"count" : 2000.0,
"distribution" : {
"state" : {
"centralMoment4" : 11285757.38865178,
"size" : 2000.0,
"centralMoment3" : 11979.395483999382,
"centralMoment2" : 103139.03800000004,
"centralMoment1" : 76.19100000000014
},
"variance" : 51.59531665832919,
"kurtosis" : 2.1271635715970967,
"mean" : 76.19100000000014,
"skewness" : 0.01618606190840656
},
"min" : "A",
"max" : "Z",
"kind" : "char"
},
"structure" : {
"type" : "character"
}
}
}
},
"pop" : {
"measure" : {
"count" : 1000.0,
"distribution" : {
"state" : {
"centralMoment4" : 2.323080620322664E+20,
"size" : 1000.0,
"centralMoment3" : 4322812032420233.5,
"centralMoment2" : 150795281801.8999,
"centralMoment1" : 8721.71000000001
},
"variance" : 150946228.02992985,
"kurtosis" : 10.267451061747597,
"mean" : 8721.71000000001,
"skewness" : 2.337959182172043
},
"min" : 0,
"max" : 94317,
"kind" : "decimal"
},
"structure" : {
"type" : "decimal"
}
},
"_id" : {
"measure" : {
"kind" : "collection",
"count" : 1000.0,
"minLength" : 5.0,
"maxLength" : 5.0
},
"structure" : {
"tag" : "_structural.string",
"type" : "array",
"of" : {
"measure" : {
"count" : 5000.0,
"distribution" : {
"state" : {
"centralMoment4" : 556673.1571508175,
"size" : 5000.0,
"centralMoment3" : 7962.505025040006,
"centralMoment2" : 38822.78220000003,
"centralMoment1" : 52.24340000000003
},
"variance" : 7.766109661932393,
"kurtosis" : 1.8485506875431554,
"mean" : 52.24340000000003,
"skewness" : 0.07362665279751003
},
"min" : "0",
"max" : "9",
"kind" : "char"
},
"structure" : {
"type" : "character"
}
}
}
},
"loc" : {
"measure" : {
"kind" : "collection",
"count" : 1000.0,
"minLength" : 2.0,
"maxLength" : 2.0
},
"structure" : {
"type" : "array",
"of" : [
{
"measure" : {
"count" : 1000.0,
"distribution" : {
"state" : {
"centralMoment4" : 281712013.3937695,
"size" : 1000.0,
"centralMoment3" : -4328243.124174622,
"centralMoment2" : 212160.04270665144,
"centralMoment1" : -90.52571468599996
},
"variance" : 212.3724151217732,
"kurtosis" : 6.290020275246902,
"mean" : -90.52571468599996,
"skewness" : -1.4027118290018674
},
"min" : -170.293408,
"max" : -67.396382,
"kind" : "decimal"
},
"structure" : {
"type" : "decimal"
}
},
{
"measure" : {
"count" : 1000.0,
"distribution" : {
"state" : {
"centralMoment4" : 3748702.702983835,
"size" : 1000.0,
"centralMoment3" : 15799.696678343358,
"centralMoment2" : 26853.295673558245,
"centralMoment1" : 39.09175202499995
},
"variance" : 26.880175849407653,
"kurtosis" : 5.224679782347093,
"mean" : 39.09175202499995,
"skewness" : 0.1137115453464455
},
"min" : 20.907097,
"max" : 65.824542,
"kind" : "decimal"
},
"structure" : {
"type" : "decimal"
}
}
]
}
}
}
}
}Schema documents represent an estimate of the structure of the given dataset and are generated from a random sample of the data. Each node of the resulting structure is annotated with the frequency the node was observed and the bounds of the observed values, when available (NB: bounds should be seen as a reference and not taken as the true, global maximum or minimum values). Additionally, for numeric values, statistical distribution information is included.
When two documents differ in structure, their differences are accumulated in a union. Basic frequency information is available for the union and more specific annotations are preserved as much as possible for the various members.
The arrayMaxLength, mapMaxSize, stringMaxLength and unionMaxSize parameters allow for control over the amount of information contained in the returned schema by limiting the size of various structures in the result. Structures that exceed the various size thresholds are compressed using various heuristics depending on the structure involved.
Paths identify files and directories in Quasar's virtual file system. File and directory paths are distinct, so /foo and /foo/ represent a file and a directory, respectively.
Depending on the backend, some restrictions may apply:
- it may be possible for a file and directory with the same name to exist side by side.
- it may not be possible for an empty directory to exist. That is, deleting the only descendant file from a directory may cause the directory to disappear as well.
- there may be limits on the overall length of paths, and/or the length of particular path segments. Any request that exceeds these limits will result in an error.
Any character can appear in a path, but when paths are embedded in character strings and byte-streams they are encoded in the following ways:
When a path appears in a request URI, or in a header such as Destination or X-FileName, it must be URL-encoded. Note: / characters that appear within path segments are encoded.
When a path appears in a JSON string value, / characters that appear within path segments are encoded as $sep$.
In both cases, the special names . and .. are encoded as $dot$ and
When only a single path segment is shown, as in the response body of a /metadata request, no special encoding is done (beyond the normal JSON encoding of " and non-ASCII characters).
For example, a file called Plan 1/2 笑 in a directory mydata would appear in the following ways:
- in a URL:
http://<host>:<port>/data/fs/mydata/Plan%201%2F2%20%E7%AC%91 - in a header:
Destination: /mydata/Plan%201%2F2%20%E7%AC%91 - in the response body of
/metadata/fs/mydata/:{ "type": "file", "name": "Plan 1/2 \u7b11" } - in an error:
{
"error": {
"status": "Path not found.",
"detail": {
"path": "/local/quasar-test/mydata/Plan 1$sep$2 \u7b11"
}
}
}Quasar produces and accepts data in two JSON-based formats or CSV (text/csv). Each JSON-based format can
represent all the types of data that Quasar supports. The two formats are appropriate for
different purposes.
Json can either be line delimited (application/ldjson/application/x-ldjson) or a single json value (application/json).
In the case of an HTTP request, it is possible to add the disposition extension to any media-type specified in an Accept header in order to receive a response with that value in the Content-Disposition header field.
Choosing between the two json formats is done using the "mode" content-type extension and by supplying either the "precise" or "readable" values. If no mode is supplied, quasar will default to the readable mode. If neither json nor csv is supplied, quasar will default to returning the results in json format. In the case of an upload request, the client MUST supply a media-type and requests without any media-type will result in an HTTP 415 error response.
This format is unambiguous, allowing every value of every type to be specified. It's useful for entering data, and for extracting data to be read by software (as opposed to people.) Contains extra information that can make it harder to read.
This format is easy to read and use with other tools, and contains minimal extra information.
It does not always convey the precise type of the source data, and does not allow all values
to be specified. For example, it's not possible to tell the difference between the string
"12:34:56" and the time value equal to 34 minutes and 56 seconds after noon.
| Type | Readable | Precise | Notes |
|---|---|---|---|
| null | null |
same | |
| boolean |
true, false
|
same | |
| string | "abc" |
same | |
| int | 1 |
same | |
| decimal | 2.1 |
same | |
| object | { "a": 1 } |
same | |
| object | { "$foo": 2 } |
{ "$obj": { "$foo": 2 } } |
Requires a type-specifier if any key starts with $. |
| array | [1, 2, 3] |
same | |
| localdatetime | "2015-01-31T10:30:00" |
{ "$localdatetime": "2015-01-31T10:30" } |
|
| localdate | "2015-01-31" |
{ "$localdate": "2015-01-31" } |
|
| localtime | "10:30:00.000" |
{ "$localtime": "10:30" } |
|
| offsetdatetime | "2015-01-31T10:30:00Z" |
{ "$offsetdatetime": "2015-01-31T10:30Z" } |
|
| offsetdate | "2015-01-31Z" |
{ "$offsetdate": "2015-01-31Z" } |
|
| offsettime | "10:30:00.000Z" |
{ "$offsettime": "10:30Z" } |
|
| interval | "PT12H34M" |
{ "$interval": "P7DT12H34M" } |
|
| binary | "TE1OTw==" |
{ "$binary": "TE1OTw==" } |
BASE64-encoded. |
When Quasar produces CSV, all fields and array elements are "flattened" so that each column in the output contains the data for a single location in the source document. For example, the document { "foo": { "bar": 1, "baz": 2 } } becomes
foo.bar,foo.baz
1,2
Data is formatted the same way as the "Readable" JSON format, except that all values including null, true, false, and numbers are indistinguishable from their string representations.
It is possible to use the columnDelimiter, rowDelimiter quoteChar and escapeChar media-type extensions keys in order to customize the layout of the csv. If some or all of these extensions are not specified, they will default to the following values:
- columnDelimiter:
, - rowDelimiter:
\r\n - quoteChar:
" - escapeChar:
"
Note: Due to the following issue in one of our dependencies. The rowDelimiter extension will be ignored for any CSV being uploaded. The rowDelimiter extension will, however, be observed for downloaded data.
Also due to this issue best to avoid non "standard" csv formats. See the MessageFormatGen.scala file for examples of which csv formats we test against.
When data is uploaded in CSV format, the headers are interpreted as field names in the same way. As with the Readable JSON format, any string that can be interpreted as another kind of value will be, so for example there's no way to specify the string "null".
First, make sure that the slamdata/quasar Github repo is building correctly (the status is displayed at the top of the README).
Then, you can try the following command:
./sbt testThis will ensure that your local version is also passing the tests.
You can also discuss issues on Discord.
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Copyright © 2014 - 2018 SlamData Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.