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from git1-us-west.apache.org (HELO git1-us-west.apache.org) (140.211.11.23) by apache.org (qpsmtpd/0.29) with ESMTP; Sat, 26 Aug 2017 05:35:47 +0000 Received: by git1-us-west.apache.org (ASF Mail Server at git1-us-west.apache.org, from userid 33) id 95673F5F2F; Sat, 26 Aug 2017 05:35:44 +0000 (UTC) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit From: imaxon@apache.org To: commits@asterixdb.apache.org Date: Sat, 26 Aug 2017 05:35:49 -0000 Message-Id: In-Reply-To: <103b462164bb438a96df81685ba6d7e8@git.apache.org> References: <103b462164bb438a96df81685ba6d7e8@git.apache.org> X-Mailer: ASF-Git Admin Mailer Subject: [06/29] asterixdb-site git commit: Update site for new release archived-at: Sat, 26 Aug 2017 05:35:53 -0000 http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/444ca1b3/docs/0.9.2/data/twm.adm ---------------------------------------------------------------------- diff --git a/docs/0.9.2/data/twm.adm b/docs/0.9.2/data/twm.adm new file mode 100644 index 0000000..df90ea6 --- /dev/null +++ b/docs/0.9.2/data/twm.adm @@ -0,0 +1,12 @@ +{"tweetid":"1","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("47.44,80.65"),"send-time":datetime("2008-04-26T10:10:00"),"referred-topics":{{"product-z","customization"}},"message-text":" love product-z its customization is good:)"} +{"tweetid":"2","user":{"screen-name":"ColineGeyer@63","lang":"en","friends_count":121,"statuses_count":362,"name":"Coline Geyer","followers_count":17159},"sender-location":point("32.84,67.14"),"send-time":datetime("2010-05-13T10:10:00"),"referred-topics":{{"ccast","shortcut-menu"}},"message-text":" like ccast its shortcut-menu is awesome:)"} +{"tweetid":"3","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("29.72,75.8"),"send-time":datetime("2006-11-04T10:10:00"),"referred-topics":{{"product-w","speed"}},"message-text":" like product-w the speed is good:)"} +{"tweetid":"4","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("39.28,70.48"),"send-time":datetime("2011-12-26T10:10:00"),"referred-topics":{{"product-b","voice-command"}},"message-text":" like product-b the voice-command is mind-blowing:)"} +{"tweetid":"5","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("40.09,92.69"),"send-time":datetime("2006-08-04T10:10:00"),"referred-topics":{{"product-w","speed"}},"message-text":" can't stand product-w its speed is terrible:("} +{"tweetid":"6","user":{"screen-name":"ColineGeyer@63","lang":"en","friends_count":121,"statuses_count":362,"name":"Coline Geyer","followers_count":17159},"sender-location":point("47.51,83.99"),"send-time":datetime("2010-05-07T10:10:00"),"referred-topics":{{"x-phone","voice-clarity"}},"message-text":" like x-phone the voice-clarity is good:)"} +{"tweetid":"7","user":{"screen-name":"ChangEwing_573","lang":"en","friends_count":182,"statuses_count":394,"name":"Chang Ewing","followers_count":32136},"sender-location":point("36.21,72.6"),"send-time":datetime("2011-08-25T10:10:00"),"referred-topics":{{"product-y","platform"}},"message-text":" like product-y the platform is good"} +{"tweetid":"8","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("46.05,93.34"),"send-time":datetime("2005-10-14T10:10:00"),"referred-topics":{{"product-z","shortcut-menu"}},"message-text":" like product-z the shortcut-menu is awesome:)"} +{"tweetid":"9","user":{"screen-name":"NathanGiesen@211","lang":"en","friends_count":39339,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416},"sender-location":point("36.86,74.62"),"send-time":datetime("2012-07-21T10:10:00"),"referred-topics":{{"ccast","voicemail-service"}},"message-text":" love ccast its voicemail-service is awesome"} +{"tweetid":"10","user":{"screen-name":"ColineGeyer@63","lang":"en","friends_count":121,"statuses_count":362,"name":"Coline Geyer","followers_count":17159},"sender-location":point("29.15,76.53"),"send-time":datetime("2008-01-26T10:10:00"),"referred-topics":{{"ccast","voice-clarity"}},"message-text":" hate ccast its voice-clarity is OMG:("} +{"tweetid":"11","user":{"screen-name":"NilaMilliron_tw","lang":"en","friends_count":445,"statuses_count":164,"name":"Nila Milliron","followers_count":22649},"sender-location":point("37.59,68.42"),"send-time":datetime("2008-03-09T10:10:00"),"referred-topics":{{"x-phone","platform"}},"message-text":" can't stand x-phone its platform is terrible"} +{"tweetid":"12","user":{"screen-name":"OliJackson_512","lang":"en","friends_count":445,"statuses_count":164,"name":"Oli Jackson","followers_count":22649},"sender-location":point("24.82,94.63"),"send-time":datetime("2010-02-13T10:10:00"),"referred-topics":{{"product-y","voice-command"}},"message-text":" like product-y the voice-command is amazing:)"} http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/444ca1b3/docs/0.9.2/data/twu.adm ---------------------------------------------------------------------- diff --git a/docs/0.9.2/data/twu.adm b/docs/0.9.2/data/twu.adm new file mode 100644 index 0000000..32a1917 --- /dev/null +++ b/docs/0.9.2/data/twu.adm @@ -0,0 +1,4 @@ +{"screen-name":"NathanGiesen@211","lang":"en","friends_count":18,"statuses_count":473,"name":"Nathan Giesen","followers_count":49416} +{"screen-name":"ColineGeyer@63","lang":"en","friends_count":121,"statuses_count":362,"name":"Coline Geyer","followers_count":17159} +{"screen-name":"NilaMilliron_tw","lang":"en","friends_count":445,"statuses_count":164,"name":"Nila Milliron","followers_count":22649} +{"screen-name":"ChangEwing_573","lang":"en","friends_count":182,"statuses_count":394,"name":"Chang Ewing","followers_count":32136} http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/444ca1b3/docs/0.9.2/datamodel.html ---------------------------------------------------------------------- diff --git a/docs/0.9.2/datamodel.html b/docs/0.9.2/datamodel.html new file mode 100644 index 0000000..f9144f0 --- /dev/null +++ b/docs/0.9.2/datamodel.html @@ -0,0 +1,916 @@ + + + + + + + + + AsterixDB – The Asterix Data Model (ADM) + + + + + + + + + + + + + + + + + +
+ + + + + +
+ + + +
+ +

The Asterix Data Model (ADM)

+
+

Table of Contents

+ + +

An instance of Asterix data model (ADM) can be a primitive type (boolean, tinyint, smallint, integer, bigint, string, float, double, date, time, datetime, etc.), a special type (null or missing), or a derived type.

+

The type names are case-insensitive, e.g., both BIGINT and bigint are acceptable.

+
+

Primitive Types

+
+

Boolean

+

boolean data type can have one of the two values: true or false.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "true": true, "false": false };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "true": true, "false": false }
+
    • +
+
+

Tinyint / Smallint / Integer (Int) / Bigint

+

Integer types using 8, 16, 32, or 64 bits. The ranges of these types are:

+ +
    + +
  • tinyint: -127 to 127
    • + +
  • smallint: -32767 to 32767
    • + +
  • integer: -2147483647 to 2147483647
    • + +
  • bigint: -9223372036854775808 to 9223372036854775807
    • +
+

int is an abbreviated alias for integer.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "tinyint": tiny("125"), "smallint": smallint("32765"), "integer": 294967295, "bigint": bigint("1700000000000000000")};
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "tinyint": 125, "smallint": 32765, "integer": 294967295, "bigint": 17000000 }
+
    • +
+
+

Float

+

float represents approximate numeric data values using 4 bytes. The range of a float value can be from 2^(-149) to (2-2^(-23)·2^(127) for both positive and negative. Beyond these ranges will get INF or -INF.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": float("NaN"), "v2": float("INF"), "v3": float("-INF"), "v4": float("-2013.5") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": NaN, "v2": Infinity, "v3": -Infinity, "v4": -2013.5 }
+
    • +
+
+

Double (double precision)

+

double represents approximate numeric data values using 8 bytes. The range of a double value can be from (2^(-1022)) to (2-2^(-52))·2^(1023) for both positive and negative. Beyond these ranges will get INF or -INF.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": double("NaN"), "v2": double("INF"), "v3": double("-INF"), "v4": "-2013.593823748327284" };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": NaN, "v2": Infinity, "v3": -Infinity, "v4": -2013.5938237483274 }
+
    • +
+

Double precision is an alias of double.

+
+

String

+

string represents a sequence of characters. The total length of the sequence can be up to 2,147,483,648.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": string("This is a string."), "v2": string("\"This is a quoted string\"") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": "This is a string.", "v2": "\"This is a quoted string\"" }
+
    • +
+
+

Binary

+

binary represents a sequence of bytes. It can be constructed from a hex or a base64 string sequence. The total length of the byte sequence can be up to 2,147,483,648.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    {
+  "hex1" : hex("ABCDEF0123456789"),
+  "hex2": hex("abcdef0123456789"),
+  "base64_1" : base64("0123456789qwertyui+/"),
+  "base64_2" : base64('QXN0ZXJpeA==')
+};
+
    • + +
  • +

    The default output format is in hex format. Thus, the expected result is:

    + +
    +
    +
    {
+  "hex1": hex("ABCDEF0123456789"),
+  "hex2": hex("ABCDEF0123456789"),
+  "base64_1": hex("D35DB7E39EBBF3DAB07ABB72BA2FBF"),
+  "base64_2": hex("41737465726978")
+}
+
    • +
+
+

Point

+

point is the fundamental two-dimensional building block for spatial types. It consists of two double coordinates x and y.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": point("80.10d, -10E5"), "v2": point("5.10E-10d, -10E5") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": point("80.1,-1000000.0"), "v2": point("5.1E-10,-1000000.0") }
+
    • +
+
+

Line

+

line consists of two points that represent the start and the end points of a line segment.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": line("10.1234,11.1e-1 +10.2E-2,-11.22"), "v2": line("0.1234,-1.00e-10 +10.5E-2,-01.02") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": line("10.1234,1.11 0.102,-11.22"), "v2": line("0.1234,-1.0E-10 0.105,-1.02") }
+
    • +
+
+

Rectangle

+

rectangle consists of two points that represent the bottom left and upper right corners of a rectangle.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": rectangle("5.1,11.8 87.6,15.6548"), "v2": rectangle("0.1234,-1.00e-10 5.5487,0.48765") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": rectangle("5.1,11.8 87.6,15.6548"), "v2": rectangle("0.1234,-1.0E-10 5.5487,0.48765") }
+
    • +
+
+

Circle

+

circle consists of one point that represents the center of the circle and a radius of type double.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": circle("10.1234,11.1e-1 +10.2E-2"), "v2": circle("0.1234,-1.00e-10 +10.5E-2") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": circle("10.1234,1.11 0.102"), "v2": circle("0.1234,-1.0E-10 0.105") }
+
    • +
+
+

Polygon

+

polygon consists of n points that represent the vertices of a simple closed polygon.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    {
+  "v1": polygon("-1.2,+1.3e2 -2.14E+5,2.15 -3.5e+2,03.6 -4.6E-3,+4.81"),
+  "v2": polygon("-1.0,+10.5e2 -02.15E+50,2.5 -1.0,+3.3e3 -2.50E+05,20.15 +3.5e+2,03.6 -4.60E-3,+4.75 -2,+1.0e2 -2.00E+5,20.10 30.5,03.25 -4.33E-3,+4.75")
+};
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    {
+  "v1": polygon("-1.2,130.0 -214000.0,2.15 -350.0,3.6 -0.0046,4.81"),
+  "v2": polygon("-1.0,1050.0 -2.15E50,2.5 -1.0,3300.0 -250000.0,20.15 350.0,3.6 -0.0046,4.75 -2.0,100.0 -200000.0,20.1 30.5,3.25 -0.00433,4.75") }
+}
+
    • +
+
+

Date

+

date represents a time point along the Gregorian calendar system specified by the year, month and day. ASTERIX supports the date from -9999-01-01 to 9999-12-31.

+

A date value can be represented in two formats, extended format and basic format.

+ +
    + +
  • Extended format is represented as [-]yyyy-mm-dd for year-month-day. Each field should be padded if there are less digits than the format specified.
    • + +
  • Basic format is in the format of [-]yyyymmdd.
    • + +
  • +

    Example:

    + +
    +
    +
    { "v1": date("2013-01-01"), "v2": date("-19700101") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": date("2013-01-01"), "v2": date("-1970-01-01") }
+
    • +
+
+

Time

+

time type describes the time within the range of a day. It is represented by three fields: hour, minute and second. Millisecond field is optional as the fraction of the second field. Its extended format is as hh:mm:ss[.mmm] and the basic format is hhmmss[mmm]. The value domain is from 00:00:00.000 to 23:59:59.999.

+

Timezone field is optional for a time value. Timezone is represented as [+|-]hh:mm for extended format or [+|-]hhmm for basic format. Note that the sign designators cannot be omitted. Z can also be used to represent the UTC local time. If no timezone information is given, it is UTC by default.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": time("12:12:12.039Z"), "v2": time("000000000-0800") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": time("12:12:12.039Z"), "v2": time("08:00:00.000Z") }
+
    • +
+
+

Datetime (Timestamp)

+

A datetime value is a combination of an date and time, representing a fixed time point along the Gregorian calendar system. The value is among -9999-01-01 00:00:00.000 and 9999-12-31 23:59:59.999.

+

A datetime value is represented as a combination of the representation of its date part and time part, separated by a separator T. Either extended or basic format can be used, and the two parts should be the same format.

+

Millisecond field and timezone field are optional, as specified in the time type.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": datetime("2013-01-01T12:12:12.039Z"), "v2": datetime("-19700101T000000000-0800") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": datetime("2013-01-01T12:12:12.039Z"), "v2": datetime("-1970-01-01T08:00:00.000Z") }
+
    • +
+

timestamp is an alias of datetime.

+
+

Duration/Year_month_duration/Day_time_duration

+

duration represents a duration of time. A duration value is specified by integers on at least one of the following fields: year, month, day, hour, minute, second, and millisecond.

+

A duration value is in the format of [-]PnYnMnDTnHnMn.mmmS. The millisecond part (as the fraction of the second field) is optional, and when no millisecond field is used, the decimal point should also be absent.

+

Negative durations are also supported for the arithmetic operations between time instance types (date, time and datetime), and is used to roll the time back for the given duration. For example date("2012-01-01") + duration("-P3D") will return date("2011-12-29").

+

There are also two sub-duration types, namely year_month_duration and day_time_duration. year_month_duration represents only the years and months of a duration, while day_time_duration represents only the day to millisecond fields. Different from the duration type, both these two subtypes are totally ordered, so they can be used for comparison and index construction.

+

Note that a canonical representation of the duration is always returned, regardless whether the duration is in the canonical representation or not from the user’s input. More information about canonical representation can be found from XPath dayTimeDuration Canonical Representation and yearMonthDuration Canonical Representation.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "v1": duration("P100Y12MT12M"), "v2": duration("-PT20.943S") };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": duration("P101YT12M"), "v2": duration("-PT20.943S") }
+
    • +
+
+

Interval

+

interval represents inclusive-exclusive ranges of time. It is defined by two time point values with the same temporal type(date, time or datetime).

+ +
    + +
  • +

    Example:

    + +
    +
    +
    {
+  "v1": interval(date("2013-01-01"), date("20130505")),
+  "v2": interval(time("00:01:01"), time("213901049+0800")),
+  "v3": interval(datetime("2013-01-01T00:01:01"), datetime("20130505T213901049+0800"))
+};
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    {
+  "v1": interval(date("2013-01-01"), date("2013-05-05")),
+  "v2": interval(time("00:01:01.000Z"), time("13:39:01.049Z")),
+  "v3": interval(datetime("2013-01-01T00:01:01.000Z"), datetime("2013-05-05T13:39:01.049Z"))
+}
+
    • +
+
+

UUID

+

uuid represents a UUID value, which stands for Universally unique identifier. It is defined by a canonical format using hexadecimal text with inserted hyphen characters. (E.g.: 5a28ce1e-6a74-4201-9e8f-683256e5706f). This type is generally used to store auto-generated primary key values.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    return { "v1":uuid("5c848e5c-6b6a-498f-8452-8847a2957421") }
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "v1": uuid("5c848e5c-6b6a-498f-8452-8847a2957421") }
+
    • +
+
+

Incomplete Information Types

+
+

Null

+

null is a special value that is often used to represent an unknown value. For example, a user might not be able to know the value of a field and let it be null.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "field": null };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    { "field": null }
+
    • +
+
+

Missing

+

missing represents a missing name-value pair in an object. If the referenced field does not exist, an empty result value is returned by the query.

+

As neither the data model nor the system enforces homogeneity for datasets or collections, items in a dataset or collection can be of heterogeneous types and so a field can be present in one object and missing in another.

+ +
    + +
  • +

    Example:

    + +
    +
    +
    { "field": missing };
+
    • +
+ +
    + +
  • +

    The expected result is:

    + +
    +
    +
    {  }
+
    • +
+

Since a field with value missing means the field is absent, we get an empty object.

+
+

Derived Types

+
+

Object

+

An object contains a set of fields, where each field is described by its name and type. An object type may be defined as either open or closed. Open objects (instances of open object types) are permitted to contain fields that are not part of the type definition, while closed objects do not permit their instances to carry extra fields. An example type definition for an object is:

+ +
+
+
    create type SoldierType as open {
+        name: string?,
+        rank: string,
+        serialno: int
+    };
+
+

Syntactically, object constructors are surrounded by curly braces “{…}”. Some examples of legitimate instances of the above type include:

+ +
+
+
    { "name": "Joe Blow", "rank": "Sergeant", "serialno": 1234567 }
+    { "rank": "Private", "serialno": 9876543 }
+    { "name": "Sally Forth", "rank": "Major", "serialno": 2345678, "gender": "F" }
+
+

The first instance has all of the type’s prescribed content. The second instance is missing the name field, which is fine because it is optional (due to the ?). The third instance has an extra field; that is fine because the type definition specifies that it is open (which is also true by default, if open is not specified). To more tightly control object content, specifying closed instead of open in the type definition for SoldierType would have made the third example instance an invalid instance of the type.

+
+

Array

+

An array is a container that holds a fixed number of values. Array constructors are denoted by brackets: “[…]”.

+

An example would be

+ +
+
+
    ["alice", 123, "bob", null]
+
+
+

Multiset

+

A multiset is a generalization of the concept of a set that, unlike a set, allows multiple instances of the multiset’s elements. Multiset constructors are denoted by two opening curly braces followed by data and two closing curly braces, like “{{…}}”.

+

An example would be

+ +
+
+
    {{"hello", 9328, "world", [1, 2, null]}}
+
+
+
+
+ +
+ +
+
+
Copyright © 2017 + The Apache Software Foundation. + All Rights Reserved. + +
+ + +
Apache AsterixDB, AsterixDB, Apache, the Apache + feather logo, and the Apache AsterixDB project logo are either + registered trademarks or trademarks of The Apache Software + Foundation in the United States and other countries. + All other marks mentioned may be trademarks or registered + trademarks of their respective owners.
+ + +
+
+ + http://git-wip-us.apache.org/repos/asf/asterixdb-site/blob/444ca1b3/docs/0.9.2/feeds/tutorial.html ---------------------------------------------------------------------- diff --git a/docs/0.9.2/feeds/tutorial.html b/docs/0.9.2/feeds/tutorial.html new file mode 100644 index 0000000..c6000c1 --- /dev/null +++ b/docs/0.9.2/feeds/tutorial.html @@ -0,0 +1,606 @@ + + + + + + + + + AsterixDB – Support for Data Ingestion in AsterixDB + + + + + + + + + + + + + + + + + +
+ + + + + +
+ + + +
+ +

Support for Data Ingestion in AsterixDB

+
+

Table of Contents

+ +
+
+

Introduction

+

In this document, we describe the support for data ingestion in AsterixDB. Data feeds are a new mechanism for having continuous data arrive into a BDMS from external sources and incrementally populate a persisted dataset and associated indexes. We add a new BDMS architectural component, called a data feed, that makes a Big Data system the caretaker for functionality that used to live outside, and we show how it improves users’ lives and system performance.

+
+

Feed Adapters

+

The functionality of establishing a connection with a data source and receiving, parsing and translating its data into ADM objects (for storage inside AsterixDB) is contained in a feed adapter. A feed adapter is an implementation of an interface and its details are specific to a given data source. An adapter may optionally be given parameters to configure its runtime behavior. Depending upon the data transfer protocol/APIs offered by the data source, a feed adapter may operate in a push or a pull mode. Push mode involves just one initial request by the adapter to the data source for setting up the connection. Once a connection is authorized, the data source “pushes” data to the adapter without any subsequent requests by the adapter. In contrast, when operating in a pull mode, the adapter makes a separate request each time to receive data. AsterixDB currently provides built-in adapters for several popular data sources such as Twitter and RSS feeds. AsterixDB addition ally provides a generic socket-based adapter that can be used to ingest data that is directed at a prescribed socket.

+

In this tutorial, we shall describe building two example data ingestion pipelines that cover the popular scenarios of ingesting data from (a) Twitter (b) RSS (c) Socket Feed source.

+
+
+

Ingesting Twitter Stream

+

We shall use the built-in push-based Twitter adapter. As a pre-requisite, we must define a Tweet using the AsterixDB Data Model (ADM) and the AsterixDB Query Language (AQL). Given below are the type definitions in AQL that create a Tweet datatype which is representative of a real tweet as obtained from Twitter.

+ +
+
+
    create dataverse feeds;
+    use dataverse feeds;
+
+    create type TwitterUser as closed {
+        screen_name: string,
+        lang: string,
+        friends_count: int32,
+        statuses_count: int32
+    };
+
+    create type Tweet as open {
+        id: int64,
+        user: TwitterUser
+    }
+
+    create dataset Tweets (Tweet)
+    primary key id;
+
+

We also create a dataset that we shall use to persist the tweets in AsterixDB. Next we make use of the create feed AQL statement to define our example data feed.

+
+
Using the “push_twitter” feed adapter
+

The “push_twitter” adapter requires setting up an application account with Twitter. To retrieve tweets, Twitter requires registering an application. Registration involves providing a name and a brief description for the application. Each application has associated OAuth authentication credentials that include OAuth keys and tokens. Accessing the Twitter API requires providing the following. 1. Consumer Key (API Key) 2. Consumer Secret (API Secret) 3. Access Token 4. Access Token Secret

+

The “push_twitter” adapter takes as configuration the above mentioned parameters. End users are required to obtain the above authentication credentials prior to using the “push_twitter” adapter. For further information on obtaining OAuth keys and tokens and registering an application with Twitter, please visit http://apps.twitter.com

+

Given below is an example AQL statement that creates a feed called “TwitterFeed” by using the “push_twitter” adapter.

+ +
+
+
    use dataverse feeds;
+
+    create feed TwitterFeed if not exists using "push_twitter"
+    (("type-name"="Tweet"),
+     ("format"="twitter-status"),
+     ("consumer.key"="************"),
+     ("consumer.secret"="**************"),
+     ("access.token"="**********"),
+     ("access.token.secret"="*************"));
+
+

It is required that the above authentication parameters are provided valid. Note that the create feed statement does not initiate the flow of data from Twitter into the AsterixDB instance. Instead, the create feed statement only results in registering the feed with the instance. The flow of data along a feed is initiated when it is connected to a target dataset using the connect feed statement and activated using the start feed statement.

+

The Twitter adapter also supports several Twitter streaming APIs as follow:

+ +
    + +
  1. Track filter (“keywords”=“AsterixDB, Apache”)
    2. + +
  2. Locations filter (“locations”=“-29.7, 79.2, 36.7, 72.0; -124.848974,-66.885444, 24.396308, 49.384358”)
    3. + +
  3. Language filter (“language”=“en”)
    4. + +
  4. Filter level (“filter-level”=“low”)
    5. +
+

An example of Twitter adapter tracking tweets with keyword “news” can be described using following ddl:

+ +
+
+
    use dataverse feeds;
+
+    create feed TwitterFeed if not exists using "push_twitter"
+    (("type-name"="Tweet"),
+     ("format"="twitter-status"),
+     ("consumer.key"="************"),
+     ("consumer.secret"="**************"),
+     ("access.token"="**********"),
+     ("access.token.secret"="*************"),
+     ("keywords"="news"));
+
+

For more details about these APIs, please visit https://dev.twitter.com/streaming/overview/request-parameters

+
+

Lifecycle of a Feed

+

A feed is a logical artifact that is brought to life (i.e., its data flow is initiated) only when it is activated using the start feed statement. Before we active a feed, we need to designate the dataset where the data to be persisted using connect feed statement. Subsequent to a connect feed statement, the feed is said to be in the connected state. After that, start feed statement will activate the feed, and start the dataflow from feed to its connected dataset. Multiple feeds can simultaneously be connected to a dataset such that the contents of the dataset represent the union of the connected feeds. Also one feed can be simultaneously connected to multiple target datasets.

+ +
+
+
    use dataverse feeds;
+
+    connect feed TwitterFeed to dataset Tweets;
+
+    start feed TwitterFeed;
+
+

The connect feed statement above directs AsterixDB to persist the data from TwitterFeed feed into the Tweets dataset. The start feed statement will activate the feed and start the dataflow. If it is required (by the high-level application) to also retain the raw tweets obtained from Twitter, the end user may additionally choose to connect TwitterFeed to a different dataset.

+

Let the feed run for a minute, then run the following query to see the latest tweets that are stored into the data set.

+ +
+
+
    use dataverse feeds;
+
+    for $i in dataset Tweets limit 10 return $i;
+
+

The dataflow of data from a feed can be terminated explicitly by stop feed statement.

+ +
+
+
    use dataverse feeds;
+
+    stop feed TwitterFeed;
+
+

The disconnnect statement can be used to disconnect the feed from certain dataset.

+ +
+
+
    use dataverse feeds;
+
+    disconnect feed TwitterFeed from dataset Tweets;
+
+
+

Ingesting with Other Adapters

+

AsterixDB has several builtin feed adapters for data ingestion. User can also implement their own adapters and plug them into AsterixDB. Here we introduce rss_feed, socket_adapter and localfs feed adapter that cover most of the common application scenarios.

+
+
+
Using the “rss_feed” feed adapter
+

rss_feed adapter allows retrieving data given a collection of RSS end point URLs. As observed in the case of ingesting tweets, it is required to model an RSS data item using AQL.

+ +
+
+
    use dataverse feeds;
+
+    create type Rss if not exists as open {
+        id: string,
+        title: string,
+        description: string,
+        link: string
+    };
+
+    create dataset RssDataset (Rss)
+    primary key id;
+
+

Next, we define an RSS feed using our built-in adapter “rss_feed”.

+ +
+
+
    use dataverse feeds;
+
+    create feed my_feed using
+    rss_feed (
+       ("type-name"="Rss"),
+       ("format"="rss"),
+       ("url"="http://rss.cnn.com/rss/edition.rss")
+    );
+
+

In the above definition, the configuration parameter “url” can be a comma-separated list that reflects a collection of RSS URLs, where each URL corresponds to an RSS endpoint or an RSS feed. The “rss_feed” retrieves data from each of the specified RSS URLs (comma separated values) in parallel.

+

The following statements connect the feed into the RssDataset:

+ +
+
+
    use dataverse feeds;
+
+    connect feed my_feed to dataset RssDataset;
+
+

The following statements activate the feed and start the dataflow:

+ +
+
+
    use dataverse feeds;
+
+    start feed my_feed;
+
+

The following statements show the latest data from the data set, stop the feed, and disconnect the feed from the data set.

+ +
+
+
    use dataverse feeds;
+
+    for $i in dataset RssDataset limit 10 return $i;
+
+    stop feed my_feed
+
+    disconnect feed my_feed from dataset RssDataset;
+
+
+
Using the “socket_adapter” feed adapter
+

socket_adapter feed opens a web socket on the given node which allows user to push data into AsterixDB directly. Here is an example:

+ +
+
+
    drop dataverse feeds if exists;
+    create dataverse feeds;
+    use dataverse feeds;
+
+    create type TestDataType as open {
+       screenName: string
+    }
+
+    create dataset TestDataset(TestDataType) primary key screenName;
+
+    create feed TestSocketFeed using socket_adapter
+    (
+       ("sockets"="127.0.0.1:10001"),
+       ("address-type"="IP"),
+       ("type-name"="TestDataType"),
+       ("format"="adm")
+    );
+
+    connect feed TestSocketFeed to dataset TestDataset;
+
+    use dataverse feeds;
+    start feed TestSocketFeed;
+
+

The above statements create a socket feed which is listening to “10001” port of the host machine. This feed accepts data records in “adm” format. As an example, you can download the sample dataset Chirp Users and push them line by line into the socket feed using any socket client you like. Following is a socket client example in Python:

+ +
+
+
    from socket import socket
+
+    ip = '127.0.0.1'
+    port1 = 10001
+    filePath = 'chu.adm'
+
+    sock1 = socket()
+    sock1.connect((ip, port1))
+
+    with open(filePath) as inputData:
+        for line in inputData:
+            sock1.sendall(line)
+        sock1.close()
+
+
+

Using the “localfs” feed adapter

+

localfs adapter enables data ingestion from local file system. It allows user to feed data records on local disk into a dataset. A DDL example for creating a localfs feed is given as follow:

+ +
+
+
    use dataverse feeds;
+
+    create type TweetType as closed {
+      id: string,
+      username : string,
+      location : string,
+      text : string,
+      timestamp : string
+    }
+
+    create dataset Tweets(TweetType)
+    primary key id;
+
+    create feed TweetFeed
+    using localfs
+    (("type-name"="TweetType"),("path"="HOSTNAME://LOCAL_FILE_PATH"),("format"="adm"))
+
+

Similar to previous examples, we need to define the datatype and dataset this feed uses. The “path” parameter refers to the local datafile that we want to ingest data from. HOSTNAME can either be the IP address or node name of the machine which holds the file. LOCAL_FILE_PATH indicates the absolute path to the file on that machine. Similarly to socket_adapter, this feed takes adm formatted data records.

+
+

Datatype for feed and target dataset

+

The “type-name” parameter in create feed statement defines the datatype of the datasource. In most use cases, feed will have the same datatype as the target dataset. However, if we want to perform certain preprocess before the data records gets into the target dataset (append autogenerated key, apply user defined functions, etc.), we will need to define the datatypes for feed and dataset separately.

+
+

Ingestion with autogenerated key

+

AsterixDB supports using autogenerated uuid as the primary key for dataset. When we use this feature, we will need to define a datatype with the primary key field, and specify that field to be autogenerated when creating the dataset. Use that same datatype in feed definition will cause a type discrepancy since there is no such field in the datasource. Thus, we will need to define two separate datatypes for feed and dataset:

+ +
+
+
    use dataverse feeds;
+
+    create type DBLPFeedType as closed {
+      dblpid: string,
+      title: string,
+      authors: string,
+      misc: string
+    }
+
+    create type DBLPDataSetType as open {
+      id: uuid,
+      dblpid: string,
+      title: string,
+      authors: string,
+      misc: string
+    }
+    create dataset DBLPDataset(DBLPDataSetType) primary key id autogenerated;
+
+    create feed DBLPFeed using socket_adapter
+    (
+        ("sockets"="127.0.0.1:10001"),
+        ("address-type"="IP"),
+        ("type-name"="DBLPFeedType"),
+        ("format"="adm")
+    );
+
+    connect feed DBLPFeed to dataset DBLPDataset;
+
+    start feed DBLPFeed;
+
+
+

Policies for Feed Ingestion

+

Multiple feeds may be concurrently operational on an AsterixDB cluster, each competing for resources (CPU cycles, network bandwidth, disk IO) to maintain pace with their respective data sources. As a data management system, AsterixDB is able to manage a set of concurrent feeds and make dynamic decisions related to the allocation of resources, resolving resource bottlenecks and the handling of failures. Each feed has its own set of constraints, influenced largely by the nature of its data source and the applications that intend to consume and process the ingested data. Consider an application that intends to discover the trending topics on Twitter by analyzing tweets that are being processed. Losing a few tweets may be acceptable. In contrast, when ingesting from a data source that provides a click-stream of ad clicks, losing data would translate to a loss of revenue for an application that tracks revenue by charging advertisers per click.

+

AsterixDB allows a data feed to have an associated ingestion policy that is expressed as a collection of parameters and associated values. An ingestion policy dictates the runtime behavior of the feed in response to resource bottlenecks and failures. AsterixDB provides a set of policies that help customize the system’s runtime behavior when handling excess objects.

+
+
+

Policies

+ +
    + +
  • +

    Spill: Objects that cannot be processed by an operator for lack of resources (referred to as excess objects hereafter) should be persisted to the local disk for deferred processing.

    • + +
  • +

    Discard: Excess objects should be discarded.

    • +
+

Note that the end user may choose to form a custom policy. For example, it is possible in AsterixDB to create a custom policy that spills excess objects to disk and subsequently resorts to throttling if the spillage crosses a configured threshold. In all cases, the desired ingestion policy is specified as part of the connect feed statement or else the “Basic” policy will be chosen as the default.

+ +
+
+
    use dataverse feeds;
+
+    connect feed TwitterFeed to dataset Tweets
+    using policy Basic;
+
+
+
+
+ +
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