Role: Lead Concept, Designer & Front-End Developer
Tools: Illustrator, Sketch
Languages Explored: HTML 5, CSS, Sass, Haml, Ruby on Rails
Awards: Hack Bushwick Crowd Favorite App
Press: Technical.Ly Brooklyn, Brooklyn Paper
This is an ongoing project and the github repo can be found here.
Oh Hey is the passive romantic's answer to online dating. Modeled to capture the wistful sentiment of a Cragislist Missed Connection, it's a romantic way to reach out into the vast sea of human hearts and see if anyone answers back.
The focus of the project was creating a web application as quickly as we could. We built the project primarily for an in-browser experience, but with a mobile-first strategy.
#60e4f5
#45f4b5
#ff5c64
#383838
Headlines
Paragraphs
Oh Hey explores the idea of the public forum. Most dating application activity exists in individual user-silos, unless two users are directly
messaging eachother. Oh Hey's reliance on the public post invites the user to sift, and engages a sense of romantic curiousity.
The hope of a user to see a message specifically for them drives them to search through the posts, but an amusement of public and anonymous displays of affection makes them stay. Even if none of the messages are for the user specifically during their visit, Oh Hey is a nice confirmation that the drive to seek romanticism is alive and well.
Oh-Hey utilizes the Google Maps API and posts are attached to locations through latitutde and longitudinal data. The posts can be searched through depending on the user's specifications.
 Oh-Hey is more specific. To initiate a conversation in Oh Hey, you first have to overcome a verification question and answer that is decided on by the inital poster.
Message verifications provide a secure way for two people to begin engaging a conversation through Oh Hey, and also narrows the funnel in which you are contacted by a potential match. This would make instances of messaging much more rare, but raises the stakes of emotional payoff for each instance due to its rarity.
The web application uses a split-screen design, so that the full right side can display a broader view of the map. Where the mobile interface minimizes map interaction, the desktop version allows the user to browse through posts by clicking directly on the points on the map.


Information
Requirements
This class assumes that you have a strong understanding of HTML and CSS. No JavaScript knowledge is expected or required.
Objectives
In this class, we will cover:
- Processing.js: Drawing with JavaScript!
- Variables
- Animation basics
- Strings
- Functions
- Logic,
if
statments, and control flow
- Loops
- Arrays
- Objects
- Object-oriented design basics
By the end of this class, you should be comfortable writing JavaScript, including events, DOM manipulation, and animation. You will be familiar with JavaScript objects and understand functions, variables, and primitive data types.
Class Materials and Schedule
We will begin day one with brief lectures on basic programming principles, pair programming, and how to use Khan Academy.
After the lectures, you'll work independently until you reach an exercise. The course materials include videos and practice sessions. Please use your headphones for all videos.
When you reach an exercise, put up a sticky note with the exercise name or number on it. A Teaching Assistant will help you find a pair partner to work on the exercise with.
If you get stuck at any point, raise your hand and a Teaching Assistant will come to help you through the problem.
Lectures
Videos and Challenges
All videos and challenges are available here.
You should watch videos and complete challenges and exercises independently.
Pair Projects
A number of exericses are labeled "Project." These exercises should be completed with a pair.
When you reach a project that requires a pair, throw up a sticky. A TA will come by and help find you a pair partner for the project.


This app allows you to make a list of all of your ToDo's and lets you help with your routine work
More features to be added soon, Keep using till then!
{{left()}} remaining of {{lists.length}}


Breakout Game
CS5850 Assignment1: Breakout Game
Yuchen Xie
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doodle time
Google Photos adding markup tool that will let you doodle on your images
And now we wait.
by Jason EnglandWhat you need to know
- Google Photos is getting a new markup tool that will allow you to doodle on your photos.
- The new tool will appear in the photo editor window between the crop and extensions buttons.
- The markup tool hasn't rolled out to many phones yet, so we'll have to be patient while we wait for Google to push it out.
Google Photos is one of the best apps you can have on your phone. It automatically backs up all of your images to the cloud, ensuring you never lose precious memories, and it even has a pretty decent photo editor built-in. Google never stops improving the app and continues to roll out new features constantly.
Most recently, we've seen Google introduce album sorting and a redesigned overflow menu in the Photos. Now, we're getting a glimpse at a new markup feature that will allow you to doodle on your photos.
Source: Julio Nihili on Twitter
The new tool hasn't shown up on any of our devices over here at Android Central, but thanks to @julionihili on Twitter, we get a couple of screenshots showing it off. As you can see from the screenshots, the new tool shows up between the crop and extensions button in the photo editor. Tapping on it brings up a menu that allows you to choose a color and two different pen options.
This is very similar to the markup tool we saw back when Android Pie launched on the Pixel 2. Back then, it was a Pixel exclusive and was reserved for the screenshot tool. Seeing it added to the Photos editor panel would be a welcomed addition, giving us a new way to interact with our photos before sharing with friends and family.
Unfortunately, it hasn't rolled out to many users yet and we all know how Google loves a staged rollout and A/B testing. Here's hoping this one makes the rounds quickly and starts spreading to more devices soon.


==== Virtualization ====
Identification virtualization services or client software allow for:
*Enumerate Virtual Machines (name, configurations, OS)
*Enumerate passwords and digital certificates for administration systems.
*Enumerate virtualization software configuration
*Configuration of Hosts
*Show risk of denial of service with control of VM state
*Access to data hosted on VM’s
*Interception of traffic of virtual hosts or services hosted on the compromised host


Tag component is used to categorize content.
Content of the tag is specified using the value property.
An icon can also be configured to be displayed next to the value with the icon property.
Different color options are available as severity levels.
Content can easily be customized with the default slot instead of using the built-in display.
| Name | Type | Default | Description |
|---|---|---|---|
| value | any | null | Value to display inside the tag. |
| severity | string | null | Severity type of the tag. |
| rounded | boolean | false | Whether the corners of the tag are rounded. |
| icon | string | null | Icon of the tag to display next to the value. |
| style | object | null | Inline style of the component. |
| styleClass | string | null | Style class of the component. |
Following is the list of structural style classes, for theming classes visit theming page.
| Name | Element |
|---|---|
| p-tag | Tag element |
| p-tag-rounded | Rounded element |
| p-tag-icon | Icon of the tag |
| p-tag-value | Value of the tag |
None.


|
CppGC
1.01
|
#include <gc.h>
Public Member Functions | |
| T & | operator* () |
| T const & | operator* () const |
| T * | operator-> () |
| T const * | operator-> () const |
| operator T * () | |
| operator T const * () const | |
| T * | operator= (T const *val) |
| bool | operator== (T const *other) |
| bool | operator== (Ref< T > const &other) |
| bool | operator!= (T const *other) |
| bool | operator!= (Ref< T > const &other) |
| Ref (T const *ptr=NULL) | |
| Ref (Ref< T > const &ref) |
Smart pointer class. This class is used to implicitly mark accessible objects. You should use this class instead of normal C++ pointers and references in all garbage collected classes
Copy constructor marks referenced objects using current allocator (if any)


qm destroy
Destroy a virtual machine in QEMU/KVM Virtual Machine Manager.
More information: home page
- Destroy a specific virtual machine:
qm destroy vm_id
- Destroy all disks that are not explicitly referenced in a specific virtual machine's configuration:
qm destroy vm_id --destroy-unreferenced-disks
- Destroy a virtual machine and remove from all locations (inventory, backup jobs, high availability managers, etc.):
qm destroy vm_id --purge
- Destroy a specific virtual machine ignoring locks and forcing destroy:
sudo qm destroy vm_id --skiplock


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Introduction
Find Influx Query Language (InfluxQL) definitions and details, including:
- Notation
- Query representation
- Letters and digits
- Identifiers
- Keywords
- Literals
- Queries
- Statements
- Clauses
- Expressions
- Other
- Query engine internals
To learn more about InfluxQL, browse the following topics:
- Explore your data with InfluxQL
- Explore your schema with InfluxQL
- Database management
- Authentication and authorization.
InfluxQL is a SQL-like query language for interacting with InfluxDB and providing features specific to storing and analyzing time series data.
Notation
The syntax is specified using Extended Backus-Naur Form (“EBNF”). EBNF is the same notation used in the Go programming language specification, which can be found here. Not so coincidentally, InfluxDB is written in Go.
Production = production_name "=" [ Expression ] "." .
Expression = Alternative { "|" Alternative } .
Alternative = Term { Term } .
Term = production_name | token [ "…" token ] | Group | Option | Repetition .
Group = "(" Expression ")" .
Option = "[" Expression "]" .
Repetition = "{" Expression "}" .
Notation operators in order of increasing precedence:
| alternation
() grouping
[] option (0 or 1 times)
{} repetition (0 to n times)
Query representation
Characters
InfluxQL is Unicode text encoded in UTF-8.
newline = /* the Unicode code point U+000A */ .
unicode_char = /* an arbitrary Unicode code point except newline */ .
Letters and digits
Letters are the set of ASCII characters plus the underscore character _ (U+005F) is considered a letter.
Only decimal digits are supported.
letter = ascii_letter | "_" .
ascii_letter = "A" … "Z" | "a" … "z" .
digit = "0" … "9" .
Identifiers
Identifiers are tokens which refer to database names, retention policy names, user names, measurement names, tag keys, and field keys.
The rules:
- double quoted identifiers can contain any unicode character other than a new line
- double quoted identifiers can contain escaped
"
characters (i.e.,\"
) - double quoted identifiers can contain InfluxQL keywords
- unquoted identifiers must start with an upper or lowercase ASCII character or “_”
- unquoted identifiers may contain only ASCII letters, decimal digits, and “_”
identifier = unquoted_identifier | quoted_identifier .
unquoted_identifier = ( letter ) { letter | digit } .
quoted_identifier = `"` unicode_char { unicode_char } `"` .
Examples
cpu
_cpu_stats
"1h"
"anything really"
"1_Crazy-1337.identifier>NAME👍"
Keywords
ALL ALTER ANY AS ASC BEGIN
BY CREATE CONTINUOUS DATABASE DATABASES DEFAULT
DELETE DESC DESTINATIONS DIAGNOSTICS DISTINCT DROP
DURATION END EVERY EXPLAIN FIELD FOR
FROM GRANT GRANTS GROUP GROUPS IN
INF INSERT INTO KEY KEYS KILL
LIMIT SHOW MEASUREMENT MEASUREMENTS NAME OFFSET
ON ORDER PASSWORD POLICY POLICIES PRIVILEGES
QUERIES QUERY READ REPLICATION RESAMPLE RETENTION
REVOKE SELECT SERIES SET SHARD SHARDS
SLIMIT SOFFSET STATS SUBSCRIPTION SUBSCRIPTIONS TAG
TO USER USERS VALUES WHERE WITH
WRITE
If you use an InfluxQL keywords as an identifier you will need to double quote that identifier in every query.
The keyword time
is a special case.
time
can be a
continuous query name,
database name,
measurement name,
retention policy name,
subscription name, and
user name.
In those cases, time
does not require double quotes in queries.
time
cannot be a field key or
tag key;
InfluxDB rejects writes with time
as a field key or tag key and returns an error.
See Frequently Asked Questions for more information.
Literals
Integers
InfluxQL supports decimal integer literals. Hexadecimal and octal literals are not currently supported.
int_lit = ( "1" … "9" ) { digit } .
Floats
InfluxQL supports floating-point literals. Exponents are not currently supported.
float_lit = int_lit "." int_lit .
Strings
String literals must be surrounded by single quotes.
Strings may contain '
characters as long as they are escaped (i.e., \'
).
string_lit = `'` { unicode_char } `'` .
Durations
Duration literals specify a length of time. An integer literal followed immediately (with no spaces) by a duration unit listed below is interpreted as a duration literal. Durations can be specified with mixed units.
Duration units
| Units | Meaning |
|---|---|
| ns | nanoseconds (1 billionth of a second) |
| u or µ | microseconds (1 millionth of a second) |
| ms | milliseconds (1 thousandth of a second) |
| s | second |
| m | minute |
| h | hour |
| d | day |
| w | week |
duration_lit = int_lit duration_unit .
duration_unit = "ns" | "u" | "µ" | "ms" | "s" | "m" | "h" | "d" | "w" .
Dates & Times
The date and time literal format is not specified in EBNF like the rest of this document. It is specified using Go’s date / time parsing format, which is a reference date written in the format required by InfluxQL. The reference date time is:
InfluxQL reference date time: January 2nd, 2006 at 3:04:05 PM
time_lit = "2006-01-02 15:04:05.999999" | "2006-01-02" .
Booleans
bool_lit = TRUE | FALSE .
Regular Expressions
regex_lit = "/" { unicode_char } "/" .
Comparators:
=~
matches against
!~
doesn’t match against
Note: InfluxQL supports using regular expressions when specifying:
- field keys and tag keys in the
SELECT
clause- measurements in the
FROM
clause- tag values and string field values in the
WHERE
clause.- tag keys in the
GROUP BY
clauseCurrently, InfluxQL does not support using regular expressions to match non-string field values in the
WHERE
clause, databases, and retention polices.
Queries
A query is composed of one or more statements separated by a semicolon.
query = statement { ";" statement } .
statement = alter_retention_policy_stmt |
create_continuous_query_stmt |
create_database_stmt |
create_retention_policy_stmt |
create_subscription_stmt |
create_user_stmt |
delete_stmt |
drop_continuous_query_stmt |
drop_database_stmt |
drop_measurement_stmt |
drop_retention_policy_stmt |
drop_series_stmt |
drop_shard_stmt |
drop_subscription_stmt |
drop_user_stmt |
explain_stmt |
explain_analyze_stmt |
grant_stmt |
kill_query_statement |
revoke_stmt |
select_stmt |
show_continuous_queries_stmt |
show_databases_stmt |
show_diagnostics_stmt |
show_field_key_cardinality_stmt |
show_field_keys_stmt |
show_grants_stmt |
show_measurement_cardinality_stmt |
show_measurement_exact_cardinality_stmt |
show_measurements_stmt |
show_queries_stmt |
show_retention_policies_stmt |
show_series_cardinality_stmt |
show_series_exact_cardinality_stmt |
show_series_stmt |
show_shard_groups_stmt |
show_shards_stmt |
show_stats_stmt |
show_subscriptions_stmt |
show_tag_key_cardinality_stmt |
show_tag_key_exact_cardinality_stmt |
show_tag_keys_stmt |
show_tag_values_stmt |
show_tag_values_cardinality_stmt |
show_users_stmt .
Statements
ALTER RETENTION POLICY
alter_retention_policy_stmt = "ALTER RETENTION POLICY" policy_name on_clause
retention_policy_option
[ retention_policy_option ]
[ retention_policy_option ]
[ retention_policy_option ] .
Examples
-- Set default retention policy for mydb to 1h.cpu.
ALTER RETENTION POLICY "1h.cpu" ON "mydb" DEFAULT
-- Change duration and replication factor.
-- REPLICATION (replication factor) not valid for OSS instances.
ALTER RETENTION POLICY "policy1" ON "somedb" DURATION 1h REPLICATION 4
CREATE CONTINUOUS QUERY
create_continuous_query_stmt = "CREATE CONTINUOUS QUERY" query_name on_clause
[ "RESAMPLE" resample_opts ]
"BEGIN" select_stmt "END" .
query_name = identifier .
resample_opts = (every_stmt for_stmt | every_stmt | for_stmt) .
every_stmt = "EVERY" duration_lit
for_stmt = "FOR" duration_lit
Examples
-- selects from DEFAULT retention policy and writes into 6_months retention policy
CREATE CONTINUOUS QUERY "10m_event_count"
ON "db_name"
BEGIN
SELECT count("value")
INTO "6_months"."events"
FROM "events"
GROUP (10m)
END;
-- this selects from the output of one continuous query in one retention policy and outputs to another series in another retention policy
CREATE CONTINUOUS QUERY "1h_event_count"
ON "db_name"
BEGIN
SELECT sum("count") as "count"
INTO "2_years"."events"
FROM "6_months"."events"
GROUP BY time(1h)
END;
-- this customizes the resample interval so the interval is queried every 10s and intervals are resampled until 2m after their start time
-- when resample is used, at least one of "EVERY" or "FOR" must be used
CREATE CONTINUOUS QUERY "cpu_mean"
ON "db_name"
RESAMPLE EVERY 10s FOR 2m
BEGIN
SELECT mean("value")
INTO "cpu_mean"
FROM "cpu"
GROUP BY time(1m)
END;
CREATE DATABASE
create_database_stmt = "CREATE DATABASE" db_name
[ WITH
[ retention_policy_duration ]
[ retention_policy_replication ]
[ retention_policy_shard_group_duration ]
[ retention_policy_name ]
] .
Replication factors do not serve a purpose with single node instances.
Examples
-- Create a database called foo
CREATE DATABASE "foo"
-- Create a database called bar with a new DEFAULT retention policy and specify the duration, replication, shard group duration, and name of that retention policy
CREATE DATABASE "bar" WITH DURATION 1d REPLICATION 1 SHARD DURATION 30m NAME "myrp"
-- Create a database called mydb with a new DEFAULT retention policy and specify the name of that retention policy
CREATE DATABASE "mydb" WITH NAME "myrp"
CREATE RETENTION POLICY
create_retention_policy_stmt = "CREATE RETENTION POLICY" policy_name on_clause
retention_policy_duration
retention_policy_replication
[ retention_policy_shard_group_duration ]
[ "DEFAULT" ] .
Replication factors do not serve a purpose with single node instances.
Examples
-- Create a retention policy.
CREATE RETENTION POLICY "10m.events" ON "somedb" DURATION 60m REPLICATION 2
-- Create a retention policy and set it as the DEFAULT.
CREATE RETENTION POLICY "10m.events" ON "somedb" DURATION 60m REPLICATION 2 DEFAULT
-- Create a retention policy and specify the shard group duration.
CREATE RETENTION POLICY "10m.events" ON "somedb" DURATION 60m REPLICATION 2 SHARD DURATION 30m
CREATE SUBSCRIPTION
Subscriptions tell InfluxDB to send all the data it receives to Kapacitor.
create_subscription_stmt = "CREATE SUBSCRIPTION" subscription_name "ON" db_name "." retention_policy "DESTINATIONS" ("ANY"|"ALL") host { "," host} .
Examples
-- Create a SUBSCRIPTION on database 'mydb' and retention policy 'autogen' that send data to 'example.com:9090' via UDP.
CREATE SUBSCRIPTION "sub0" ON "mydb"."autogen" DESTINATIONS ALL 'udp://example.com:9090'
-- Create a SUBSCRIPTION on database 'mydb' and retention policy 'autogen' that round robins the data to 'h1.example.com:9090' and 'h2.example.com:9090'.
CREATE SUBSCRIPTION "sub0" ON "mydb"."autogen" DESTINATIONS ANY 'udp://h1.example.com:9090', 'udp://h2.example.com:9090'
CREATE USER
create_user_stmt = "CREATE USER" user_name "WITH PASSWORD" password
[ "WITH ALL PRIVILEGES" ] .
Examples
-- Create a normal database user.
CREATE USER "jdoe" WITH PASSWORD '1337password'
-- Create an admin user.
-- Note: Unlike the GRANT statement, the "PRIVILEGES" keyword is required here.
CREATE USER "jdoe" WITH PASSWORD '1337password' WITH ALL PRIVILEGES
Note: The password string must be wrapped in single quotes.
DELETE
delete_stmt = "DELETE" ( from_clause | where_clause | from_clause where_clause ) .
Examples
DELETE FROM "cpu"
DELETE FROM "cpu" WHERE time < '2000-01-01T00:00:00Z'
DELETE WHERE time < '2000-01-01T00:00:00Z'
DROP CONTINUOUS QUERY
drop_continuous_query_stmt = "DROP CONTINUOUS QUERY" query_name on_clause .
Example
DROP CONTINUOUS QUERY "myquery" ON "mydb"
DROP DATABASE
drop_database_stmt = "DROP DATABASE" db_name .
Example
DROP DATABASE "mydb"
DROP MEASUREMENT
drop_measurement_stmt = "DROP MEASUREMENT" measurement .
Examples
-- drop the cpu measurement
DROP MEASUREMENT "cpu"
DROP RETENTION POLICY
drop_retention_policy_stmt = "DROP RETENTION POLICY" policy_name on_clause .
Example
-- drop the retention policy named 1h.cpu from mydb
DROP RETENTION POLICY "1h.cpu" ON "mydb"
DROP SERIES
drop_series_stmt = "DROP SERIES" ( from_clause | where_clause | from_clause where_clause ) .
Note: Filtering by time is not supported in the
WHERE
clause.
Example
DROP SERIES FROM "telegraf"."autogen"."cpu" WHERE cpu = 'cpu8'
DROP SHARD
drop_shard_stmt = "DROP SHARD" ( shard_id ) .
Example
DROP SHARD 1
DROP SUBSCRIPTION
drop_subscription_stmt = "DROP SUBSCRIPTION" subscription_name "ON" db_name "." retention_policy .
Example
DROP SUBSCRIPTION "sub0" ON "mydb"."autogen"
DROP USER
drop_user_stmt = "DROP USER" user_name .
Example
DROP USER "jdoe"
EXPLAIN
Parses and plans the query, and then prints a summary of estimated costs.
Many SQL engines use the EXPLAIN
statement to show join order, join algorithms, and predicate and expression pushdown.
Since InfluxQL does not support joins, the cost of a InfluxQL query is typically a function of the total series accessed, the number of iterator accesses to a TSM file, and the number of TSM blocks that need to be scanned.
The elements of EXPLAIN
query plan include:
- expression
- auxillary fields
- number of shards
- number of series
- cached values
- number of files
- number of blocks
- size of blocks
explain_stmt = "EXPLAIN" select_stmt .
Example
> explain select sum(pointReq) from "_internal"."monitor"."write" group by hostname;
> QUERY PLAN
------
EXPRESSION: sum(pointReq::integer)
NUMBER OF SHARDS: 2
NUMBER OF SERIES: 2
CACHED VALUES: 110
NUMBER OF FILES: 1
NUMBER OF BLOCKS: 1
SIZE OF BLOCKS: 931
EXPLAIN ANALYZE
Executes the query and counts the actual costs during runtime.
explain_analyze_stmt = "EXPLAIN ANALYZE" select_stmt .
Example
> explain analyze select sum(pointReq) from "_internal"."monitor"."write" group by hostname;
> EXPLAIN ANALYZE
-----------
.
└── select
├── execution_time: 242.167µs
├── planning_time: 2.165637ms
├── total_time: 2.407804ms
└── field_iterators
├── labels
│ └── statement: SELECT sum(pointReq::integer) FROM "_internal"."monitor"."write" GROUP BY hostname
└── expression
├── labels
│ └── expr: sum(pointReq::integer)
├── create_iterator
│ ├── labels
│ │ ├── measurement: write
│ │ └── shard_id: 57
│ ├── cursors_ref: 1
│ ├── cursors_aux: 0
│ ├── cursors_cond: 0
│ ├── float_blocks_decoded: 0
│ ├── float_blocks_size_bytes: 0
│ ├── integer_blocks_decoded: 1
│ ├── integer_blocks_size_bytes: 931
│ ├── unsigned_blocks_decoded: 0
│ ├── unsigned_blocks_size_bytes: 0
│ ├── string_blocks_decoded: 0
│ ├── string_blocks_size_bytes: 0
│ ├── boolean_blocks_decoded: 0
│ ├── boolean_blocks_size_bytes: 0
│ └── planning_time: 1.401099ms
└── create_iterator
├── labels
│ ├── measurement: write
│ └── shard_id: 58
├── cursors_ref: 1
├── cursors_aux: 0
├── cursors_cond: 0
├── float_blocks_decoded: 0
├── float_blocks_size_bytes: 0
├── integer_blocks_decoded: 0
├── integer_blocks_size_bytes: 0
├── unsigned_blocks_decoded: 0
├── unsigned_blocks_size_bytes: 0
├── string_blocks_decoded: 0
├── string_blocks_size_bytes: 0
├── boolean_blocks_decoded: 0
├── boolean_blocks_size_bytes: 0
└── planning_time: 76.192µs
GRANT
NOTE: Users can be granted privileges on databases that do not exist.
grant_stmt = "GRANT" privilege [ on_clause ] to_clause .

Where query_id
is the query ID, displayed in the SHOW QUERIES
output as qid
.

Examples
-- revoke admin privileges from jdoe
REVOKE ALL PRIVILEGES FROM "jdoe"
-- revoke read privileges from jdoe on mydb
REVOKE READ ON "mydb" FROM "jdoe"
SELECT
select_stmt = "SELECT" fields from_clause [ into_clause ] [ where_clause ]
[ group_by_clause ] [ order_by_clause ] [ limit_clause ]
[ offset_clause ] [ slimit_clause ] [ soffset_clause ] [ timezone_clause ] .
Examples
Select from all measurements beginning with cpu into the same measurement name in the cpu_1h retention policy
SELECT mean("value") INTO "cpu_1h".:MEASUREMENT FROM /cpu.*/
Select from measurements grouped by the day with a timezone
SELECT mean("value") FROM "cpu" GROUP BY region, time(1d) fill(0) tz('America/Chicago')
SHOW CARDINALITY
Refers to the group of commands used to estimate or count exactly the cardinality of measurements, series, tag keys, tag key values, and field keys.
The SHOW CARDINALITY commands are available in two variations: estimated and exact. Estimated values are calculated using sketches and are a safe default for all cardinality sizes. Exact values are counts directly from TSM (Time-Structured Merge Tree) data, but are expensive to run for high cardinality data. Unless required, use the estimated variety.
Filtering by time
is only supported when Time Series Index (TSI) is enabled on a database.
See the specific SHOW CARDINALITY commands for details:
- SHOW FIELD KEY CARDINALITY
- SHOW MEASUREMENT CARDINALITY
- SHOW SERIES CARDINALITY
- SHOW TAG KEY CARDINALITY
- SHOW TAG VALUES CARDINALITY
SHOW CONTINUOUS QUERIES
show_continuous_queries_stmt = "SHOW CONTINUOUS QUERIES" .
Example
-- show all continuous queries
SHOW CONTINUOUS QUERIES
SHOW DATABASES
show_databases_stmt = "SHOW DATABASES" .
Example
-- show all databases
SHOW DATABASES
SHOW DIAGNOSTICS
Displays node information, such as build information, uptime, hostname, server configuration, memory usage, and Go runtime diagnostics.
For more information on using the SHOW DIAGNOSTICS
command, see Using the SHOW DIAGNOSTICS command for monitoring InfluxDB.
show_diagnostics_stmt = "SHOW DIAGNOSTICS"
SHOW FIELD KEY CARDINALITY
Estimates or counts exactly the cardinality of the field key set for the current database unless a database is specified using the ON <database>
option.
Note:
ON <database>
,FROM <sources>
,WITH KEY = <key>
,WHERE <condition>
,GROUP BY <dimensions>
, andLIMIT/OFFSET
clauses are optional. When using these query clauses, the query falls back to an exact count. Filtering bytime
is only supported when Time Series Index (TSI) is enabled andtime
is not supported in theWHERE
clause.
show_field_key_cardinality_stmt = "SHOW FIELD KEY CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
show_field_key_exact_cardinality_stmt = "SHOW FIELD KEY EXACT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
Examples
-- show estimated cardinality of the field key set of current database
SHOW FIELD KEY CARDINALITY
-- show exact cardinality on field key set of specified database
SHOW FIELD KEY EXACT CARDINALITY ON mydb
SHOW FIELD KEYS
show_field_keys_stmt = "SHOW FIELD KEYS" [on_clause] [ from_clause ] .
Examples
-- show field keys and field value data types from all measurements
SHOW FIELD KEYS
-- show field keys and field value data types from specified measurement
SHOW FIELD KEYS FROM "cpu"
SHOW GRANTS
show_grants_stmt = "SHOW GRANTS FOR" user_name .
Example
-- show grants for jdoe
SHOW GRANTS FOR "jdoe"
SHOW MEASUREMENT CARDINALITY
Estimates or counts exactly the cardinality of the measurement set for the current database unless a database is specified using the ON <database>
option.
Note:
ON <database>
,FROM <sources>
,WITH KEY = <key>
,WHERE <condition>
,GROUP BY <dimensions>
, andLIMIT/OFFSET
clauses are optional. When using these query clauses, the query falls back to an exact count. Filtering bytime
is only supported when TSI (Time Series Index) is enabled andtime
is not supported in theWHERE
clause.
show_measurement_cardinality_stmt = "SHOW MEASUREMENT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
show_measurement_exact_cardinality_stmt = "SHOW MEASUREMENT EXACT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
Example
-- show estimated cardinality of measurement set on current database
SHOW MEASUREMENT CARDINALITY
-- show exact cardinality of measurement set on specified database
SHOW MEASUREMENT EXACT CARDINALITY ON mydb
SHOW MEASUREMENTS
show_measurements_stmt = "SHOW MEASUREMENTS" [on_clause] [ with_measurement_clause ] [ where_clause ] [ limit_clause ] [ offset_clause ] .
Examples
-- show all measurements
SHOW MEASUREMENTS
-- show measurements where region tag = 'uswest' AND host tag = 'serverA'
SHOW MEASUREMENTS WHERE "region" = 'uswest' AND "host" = 'serverA'
-- show measurements that start with 'h2o'
SHOW MEASUREMENTS WITH MEASUREMENT =~ /h2o.*/
SHOW QUERIES
show_queries_stmt = "SHOW QUERIES" .
Example
-- show all currently-running queries
SHOW QUERIES
--
SHOW RETENTION POLICIES
show_retention_policies_stmt = "SHOW RETENTION POLICIES" [on_clause] .
Example
-- show all retention policies on a database
SHOW RETENTION POLICIES ON "mydb"
SHOW SERIES
show_series_stmt = "SHOW SERIES" [on_clause] [ from_clause ] [ where_clause ] [ limit_clause ] [ offset_clause ] .
Example
SHOW SERIES FROM "telegraf"."autogen"."cpu" WHERE cpu = 'cpu8'
SHOW SERIES CARDINALITY
Estimates or counts exactly the cardinality of the series for the current database unless a database is specified using the ON <database>
option.
Series cardinality is the major factor that affects RAM requirements. For more information, see:
Note:
ON <database>
,FROM <sources>
,WITH KEY = <key>
,WHERE <condition>
,GROUP BY <dimensions>
, andLIMIT/OFFSET
clauses are optional. When using these query clauses, the query falls back to an exact count. Filtering bytime
is not supported in theWHERE
clause.
show_series_cardinality_stmt = "SHOW SERIES CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
show_series_exact_cardinality_stmt = "SHOW SERIES EXACT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
Examples
-- show estimated cardinality of the series on current database
SHOW SERIES CARDINALITY
-- show estimated cardinality of the series on specified database
SHOW SERIES CARDINALITY ON mydb
-- show exact series cardinality
SHOW SERIES EXACT CARDINALITY
-- show series cardinality of the series on specified database
SHOW SERIES EXACT CARDINALITY ON mydb
SHOW SHARD GROUPS
show_shard_groups_stmt = "SHOW SHARD GROUPS" .
Example
SHOW SHARD GROUPS
SHOW SHARDS
show_shards_stmt = "SHOW SHARDS" .
Example
SHOW SHARDS
SHOW STATS
Returns detailed statistics on available components of an InfluxDB node and available (enabled) components.
For more information on using the SHOW STATS
command, see Using the SHOW STATS command to monitor InfluxDB.
show_stats_stmt = "SHOW STATS [ FOR '<component>' | 'indexes' ]"
SHOW STATS
- The
SHOW STATS
command does not list index memory usage – use theSHOW STATS FOR 'indexes'
command. - Statistics returned by
SHOW STATS
are stored in memory and reset to zero when the node is restarted, butSHOW STATS
is triggered every 10 seconds to populate the_internal
database.
SHOW STATS FOR <component>
- For the specified component (<component>), the command returns available statistics.
- For the
runtime
component, the command returns an overview of memory usage by the InfluxDB system, using the Go runtime package.
SHOW STATS FOR 'indexes'
- Returns an estimate of memory use of all indexes. Index memory use is not reported with
SHOW STATS
because it is a potentially expensive operation.
Example
> show stats
name: runtime
-------------
Alloc Frees HeapAlloc HeapIdle HeapInUse HeapObjects HeapReleased HeapSys Lookups Mallocs NumGC NumGoroutine PauseTotalNs Sys TotalAlloc
4136056 6684537 4136056 34586624 5816320 49412 0 40402944<PHONE_NUMBER> 83 44 36083006 46692600 439945704
name: graphite
tags: proto=tcp
batches_tx bytes_rx connections_active connections_handled points_rx points_tx
---------- -------- ------------------ ------------------- --------- ---------
159 3999750 0 1<PHONE_NUMBER>10
SHOW SUBSCRIPTIONS
show_subscriptions_stmt = "SHOW SUBSCRIPTIONS" .
Example
SHOW SUBSCRIPTIONS
SHOW TAG KEY CARDINALITY
Estimates or counts exactly the cardinality of tag key set on the current database unless a database is specified using the ON <database>
option.
Note:
ON <database>
,FROM <sources>
,WITH KEY = <key>
,WHERE <condition>
,GROUP BY <dimensions>
, andLIMIT/OFFSET
clauses are optional. When using these query clauses, the query falls back to an exact count. Filtering bytime
is only supported when TSI (Time Series Index) is enabled andtime
is not supported in theWHERE
clause.
show_tag_key_cardinality_stmt = "SHOW TAG KEY CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
show_tag_key_exact_cardinality_stmt = "SHOW TAG KEY EXACT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ]
Examples
-- show estimated tag key cardinality
SHOW TAG KEY CARDINALITY
-- show exact tag key cardinality
SHOW TAG KEY EXACT CARDINALITY
SHOW TAG KEYS
show_tag_keys_stmt = "SHOW TAG KEYS" [on_clause] [ from_clause ] [ where_clause ]
[ limit_clause ] [ offset_clause ] .
Examples
-- show all tag keys
SHOW TAG KEYS
-- show all tag keys from the cpu measurement
SHOW TAG KEYS FROM "cpu"
-- show all tag keys from the cpu measurement where the region key = 'uswest'
SHOW TAG KEYS FROM "cpu" WHERE "region" = 'uswest'
-- show all tag keys where the host key = 'serverA'
SHOW TAG KEYS WHERE "host" = 'serverA'
SHOW TAG VALUES
show_tag_values_stmt = "SHOW TAG VALUES" [on_clause] [ from_clause ] with_tag_clause [ where_clause ]
[ limit_clause ] [ offset_clause ] .
Examples
-- show all tag values across all measurements for the region tag
SHOW TAG VALUES WITH KEY = "region"
-- show tag values from the cpu measurement for the region tag
SHOW TAG VALUES FROM "cpu" WITH KEY = "region"
-- show tag values across all measurements for all tag keys that do not include the letter c
SHOW TAG VALUES WITH KEY !~ /.*c.*/
-- show tag values from the cpu measurement for region & host tag keys where service = 'redis'
SHOW TAG VALUES FROM "cpu" WITH KEY IN ("region", "host") WHERE "service" = 'redis'
SHOW TAG VALUES CARDINALITY
Estimates or counts exactly the cardinality of tag key values for the specified tag key on the current database unless a database is specified using the ON <database>
option.
Note:
ON <database>
,FROM <sources>
,WITH KEY = <key>
,WHERE <condition>
,GROUP BY <dimensions>
, andLIMIT/OFFSET
clauses are optional. When using these query clauses, the query falls back to an exact count. Filtering bytime
is only supported when TSI (Time Series Index) is enabled.
show_tag_values_cardinality_stmt = "SHOW TAG VALUES CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ] with_key_clause
show_tag_values_exact_cardinality_stmt = "SHOW TAG VALUES EXACT CARDINALITY" [ on_clause ] [ from_clause ] [ where_clause ] [ group_by_clause ] [ limit_clause ] [ offset_clause ] with_key_clause
Examples
-- show estimated tag key values cardinality for a specified tag key
SHOW TAG VALUES CARDINALITY WITH KEY = "myTagKey"
-- show estimated tag key values cardinality for a specified tag key
SHOW TAG VALUES CARDINALITY WITH KEY = "myTagKey"
-- show exact tag key values cardinality for a specified tag key
SHOW TAG VALUES EXACT CARDINALITY WITH KEY = "myTagKey"
-- show exact tag key values cardinality for a specified tag key
SHOW TAG VALUES EXACT CARDINALITY WITH KEY = "myTagKey"
SHOW USERS
show_users_stmt = "SHOW USERS" .
Example
-- show all users
SHOW USERS
Clauses
from_clause = "FROM" measurements .
group_by_clause = "GROUP BY" dimensions fill(fill_option).
into_clause = "INTO" ( measurement | back_ref ).
limit_clause = "LIMIT" int_lit .
offset_clause = "OFFSET" int_lit .
slimit_clause = "SLIMIT" int_lit .
soffset_clause = "SOFFSET" int_lit .
timezone_clause = tz(string_lit) .
on_clause = "ON" db_name .
order_by_clause = "ORDER BY" sort_fields .
to_clause = "TO" user_name .
where_clause = "WHERE" expr .
with_measurement_clause = "WITH MEASUREMENT" ( "=" measurement | "=~" regex_lit ) .
with_tag_clause = "WITH KEY" ( "=" tag_key | "!=" tag_key | "=~" regex_lit | "IN (" tag_keys ")" ) .
Expressions
binary_op = "+" | "-" | "*" | "/" | "%" | "&" | "|" | "^" | "AND" |
"OR" | "=" | "!=" | "<>" | "<" | "<=" | ">" | ">=" .
expr = unary_expr { binary_op unary_expr } .
unary_expr = "(" expr ")" | var_ref | time_lit | string_lit | int_lit |
float_lit | bool_lit | duration_lit | regex_lit .
Other
alias = "AS" identifier .
back_ref = ( policy_name ".:MEASUREMENT" ) |
( db_name "." [ policy_name ] ".:MEASUREMENT" ) .
db_name = identifier .
dimension = expr .
dimensions = dimension { "," dimension } .
field_key = identifier .
field = expr [ alias ] .
fields = field { "," field } .
fill_option = "null" | "none" | "previous" | int_lit | float_lit | "linear" .
host = string_lit .
measurement = measurement_name |
( policy_name "." measurement_name ) |
( db_name "." [ policy_name ] "." measurement_name ) .
measurements = measurement { "," measurement } .
measurement_name = identifier | regex_lit .
password = string_lit .
policy_name = identifier .
privilege = "ALL" [ "PRIVILEGES" ] | "READ" | "WRITE" .
query_id = int_lit .
query_name = identifier .
retention_policy = identifier .
retention_policy_option = retention_policy_duration |
retention_policy_replication |
retention_policy_shard_group_duration |
"DEFAULT" .
retention_policy_duration = "DURATION" duration_lit .
retention_policy_replication = "REPLICATION" int_lit .
retention_policy_shard_group_duration = "SHARD DURATION" duration_lit .
retention_policy_name = "NAME" identifier .
series_id = int_lit .
shard_id = int_lit .
sort_field = field_key [ ASC | DESC ] .
sort_fields = sort_field { "," sort_field } .
subscription_name = identifier .
tag_key = identifier .
tag_keys = tag_key { "," tag_key } .
user_name = identifier .
var_ref = measurement .
Comments
Use comments with InfluxQL statements to describe your queries.
- A single line comment begins with two hyphens (
--
) and ends where InfluxDB detects a line break. This comment type cannot span several lines. - A multi-line comment begins with
/*
and ends with*/
. This comment type can span several lines. Multi-line comments do not support nested multi-line comments.
Query Engine Internals
Once you understand the language itself, it’s important to know how these language constructs are implemented in the query engine. This gives you an intuitive sense for how results will be processed and how to create efficient queries.
The life cycle of a query looks like this:
InfluxQL query string is tokenized and then parsed into an abstract syntax tree (AST). This is the code representation of the query itself.
The AST is passed to the
QueryExecutor
which directs queries to the appropriate handlers. For example, queries related to meta data are executed by the meta service andSELECT
statements are executed by the shards themselves.The query engine then determines the shards that match the
SELECT
statement’s time range. From these shards, iterators are created for each field in the statement.Iterators are passed to the emitter which drains them and joins the resulting points. The emitter’s job is to convert simple time/value points into the more complex result objects that are returned to the client.
Understanding iterators
Iterators are at the heart of the query engine. They provide a simple interface for looping over a set of points. For example, this is an iterator over Float points:
type FloatIterator interface {
Next() *FloatPoint
}
These iterators are created through the IteratorCreator
interface:
type IteratorCreator interface {
CreateIterator(opt *IteratorOptions) (Iterator, error)
}
The IteratorOptions
provide arguments about field selection, time ranges,
and dimensions that the iterator creator can use when planning an iterator.
The IteratorCreator
interface is used at many levels such as the Shards
,
Shard
, and Engine
. This allows optimizations to be performed when applicable
such as returning a precomputed COUNT()
.
Iterators aren’t just for reading raw data from storage though. Iterators can be
composed so that they provided additional functionality around an input
iterator. For example, a DistinctIterator
can compute the distinct values for
each time window for an input iterator. Or a FillIterator
can generate
additional points that are missing from an input iterator.
This composition also lends itself well to aggregation. For example, a statement such as this:
SELECT MEAN(value) FROM cpu GROUP BY time(10m)
In this case, MEAN(value)
is a MeanIterator
wrapping an iterator from the
underlying shards. However, if we can add an additional iterator to determine
the derivative of the mean:
SELECT DERIVATIVE(MEAN(value), 20m) FROM cpu GROUP BY time(10m)
Understanding auxiliary fields
Because InfluxQL allows users to use selector functions such as FIRST()
,
LAST()
, MIN()
, and MAX()
, the engine must provide a way to return related
data at the same time with the selected point.
For example, in this query:
SELECT FIRST(value), host FROM cpu GROUP BY time(1h)
We are selecting the first value
that occurs every hour but we also want to
retrieve the host
associated with that point. Since the Point
types only
specify a single typed Value
for efficiency, we push the host
into the
auxiliary fields of the point. These auxiliary fields are attached to the point
until it is passed to the emitter where the fields get split off to their own
iterator.
Built-in iterators
There are many helper iterators that let us build queries:
Merge Iterator - This iterator combines one or more iterators into a single new iterator of the same type. This iterator guarantees that all points within a window will be output before starting the next window but does not provide ordering guarantees within the window. This allows for fast access for aggregate queries which do not need stronger sorting guarantees.
Sorted Merge Iterator - This iterator also combines one or more iterators into a new iterator of the same type. However, this iterator guarantees time ordering of every point. This makes it slower than the
MergeIterator
but this ordering guarantee is required for non-aggregate queries which return the raw data points.Limit Iterator - This iterator limits the number of points per name/tag group. This is the implementation of the
LIMIT
&OFFSET
syntax.Fill Iterator - This iterator injects extra points if they are missing from the input iterator. It can provide
null
points, points with the previous value, or points with a specific value.Buffered Iterator - This iterator provides the ability to “unread” a point back onto a buffer so it can be read again next time. This is used extensively to provide lookahead for windowing.
Reduce Iterator - This iterator calls a reduction function for each point in a window. When the window is complete then all points for that window are output. This is used for simple aggregate functions such as
COUNT()
.Reduce Slice Iterator - This iterator collects all points for a window first and then passes them all to a reduction function at once. The results are returned from the iterator. This is used for aggregate functions such as
DERIVATIVE()
.Transform Iterator - This iterator calls a transform function for each point from an input iterator. This is used for executing binary expressions.
Dedupe Iterator - This iterator only outputs unique points. It is resource intensive so it is only used for small queries such as meta query statements.
Call iterators
Function calls in InfluxQL are implemented at two levels. Some calls can be
wrapped at multiple layers to improve efficiency. For example, a COUNT()
can
be performed at the shard level and then multiple CountIterator
s can be
wrapped with another CountIterator
to compute the count of all shards. These
iterators can be created using NewCallIterator()
.
Some iterators are more complex or need to be implemented at a higher level.
For example, the DERIVATIVE()
needs to retrieve all points for a window first
before performing the calculation. This iterator is created by the engine itself
and is never requested to be created by the lower levels.


Understanding how your tweaks get enabled in Electra.
The trigger
It all started from this tweet:
If you want to enable tweaks after jailbreaking without tweaks on Electra: pic.twitter.com/eA1GqxJbGd
— Jake James (@Jakeashacks) July 18, 2018
Before some developer wraps this procedure into an useful tweak, I tried to understand what’s actually happening there. Luckily, CoolStar had just open-sourced an Electra1131 build a couple hours before Jake’s tweet. Thank you, CoolStar!
The magic line
inject_criticald 1 /electra/pspawn_payload.dylib
Its arguments are pretty simple: take a pid (in our case, pid 1 equals launchd) and inject a library into it.
Down the rabbit hole
Most subroutines are already described in previously released Ian Beer’s exploitations:
Take launchd pid -> get a task port # -> achieve tfp0.
Since we have root access (and now, tfp0), the injection can be achieved.
We proceed to find the start address of *launchd* process by using Ian Beer’s binary_load_address (it grabs the address from the second argument of mach_vm_region. Check it at triple_fetch for a deep understanding).
We’ve arrived at the first console logging line (Address is at 0000…).
Here’s the final inject_criticald procedure call:
call_remote(remoteTask, dlopen, 2, REMOTE_CSTRING(loaded_dylib), REMOTE_LITERAL(RTLD_NOW));
This procedure uses dlopen()
function to load our library into launchd. The RTLD_NOW flag makes sure all undefined symbols in the library are resolved before dlopen() returns.
Somewhere along the call_remote road, the second console logging line warns us about the success of the find_blr_x19_gadget()
function, which has to do with ROP and the inner workings of (again) Ian Beer’s triple_fetch exploit.
Our loaded_dylib (/electra/pspawn_payload.dylib, if you remember well) is successfully injected :)
Why dylibbin’?
Here’s a brief look into *pspawn_payload* bin:
There it is:
The rebindings described allow us to fake spawn Daemons (more specifically, every Daemon found at /Library/LaunchDaemons/), including our dearest /usr/libexec/cydia/startup, which activates our tweaks without (luckily!) causing panics/loops that would require a reboot.
Since we’re already at a jailbroken state, CoolStar’s *jailbreakd* Daemon is already active before these steps — and thus, it is skipped. The same goes on for *sshd* (OpenSSH).
Making tweaks appear (Windows’ “restart to apply changes”)
If we compare Jakes’ flow against CoolStar’s, we’ll see the only diff is the post injecting process to make changes apply: while Jake’s only resprings the device, CoolStar’s does a wider cleaning by using ldrestart
as described in his tweet:
Little known tip: run “ldrestart” as root to restart iOS without rebooting the kernel or exiting jailbroken state (preserves uptime)
— CoolStar (@coolstarorg) May 3, 2018
That’s all, folks. Thank you for reading this far :)
Special thanks to:
Coolstar and Electra Team for an amazing jailbreak tool
Ian Beer for amazing iOS kernel research
Jake James for provoking my curiosity
Jay Freeman for battling jailbreak legality for all these years
Apple for the most amazing mobile OS ever


2 writes to _paths
Microsoft.PowerShell.Commands.Utility (2)
commands\utility\GetHash.cs (2)
36
_paths
= value;
57
_paths
= value;
4 references to _paths
Microsoft.PowerShell.Commands.Utility (4)
commands\utility\GetHash.cs (4)
31
return
_paths
;
52
return
_paths
;
93
foreach (string path in
_paths
)
118
foreach (string path in
_paths
)


Since FIESG is a little community, everyone who particpated is the member of this group.
- Would you like to improve this group?
- How should we do to make it different?
Ps: “imporve” does not mean the current situation is bad, it just mean being better.


Task
Day
Reset Input
Create
Update
Delete
Clear Database (WARNING)
List of tasks to do on certain days
Tasks
Days


I was given enough information to complete the task.
It was clear what was being asked.
The task was relevant to me.
The task was easy to understand.
I was engaged in the task.
I performed the task to the best of my ability.
I found the task difficult.
I am confident the content I found satisfied the task.
I am confident about the search query terms I used.
I am confident I identified relevant websites from the search results.
I am confident in my ability to read the website content.
I am confident in my ability to understand the content of the websites I visited.
I am confident the search task was completed.
Submit questionnaire to proceed


If your set is not TreeSet implementation, you have the following options to sort your set collection.
For example, suppose that you have a HashSet collection as shown below;
Set hashSet = new HashSet();
hashSet.add("murat");
hashSet.add("ayse");
hashSet.add("oguz");
You can one of these methods to sort your set collection;
1. One method is to create a new TreeSet whose constructor has any collection type.
TreeSet treeSet = new TreeSet(hashSet);
for (final String element : treeSet) {
System.out.println(element);
}
2. Another option is to create a new ArrayList whose constructor has any collection type and sort this List via Collections.sort(List) method.
List list = new ArrayList(hashSet);
Collections.sort(list);
for (final String element : list) {
System.out.println(element);
}
3. Other option is to convert this set into array through the use of toArray method and sort this array via Arrays.sort(Object[]) method.
Object[] array = hashSet.toArray();
Arrays.sort(array);
for (final Object element : array) {
System.out.println(element);
}
All these options shown above will have the same output.


使用AWS S3 key / bucket推送数据集到VDN
If you intened to make it public, make sure that requester pay is enabled AND access policies are set correctly for the entire bucket. Provide AWS credential via environment variables or by running aws configure inside container or via IAM roles on EC2.


We aim to rapidly publish high impact research publications and openly share our data as part of our commitment to supporting pre-competitive research in drug discovery. We also publish review articles on relevant topics in drug discovery.
There are no research publications available for this year.
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{{ publication.publication_name }}, {{ publication.volume }}, {{ publication.pages }}
PubMed: {{ publication.pmid }} | DOI: {{ publication.doi }}
There are no review articles available for this year.
{% else %} {% for publication in all_2015_review_articles %}{{ publication.title }}
{{ publication.author }}
{{ publication.publication_name }}, {{ publication.volume }}, {{ publication.pages }}
PubMed: {{ publication.pmid }} | DOI: {{ publication.doi }}


The current approach of the OWASP CSRFGuard relies on JavaScript logic for injecting CSRF tokens into HTML elements or XHR requests. Forcing synchronous loading of the AJAX requests has been disabled, since they were deprecated due to their negative impact on the user experience. For this reason, protecting resources that would load before or in parallel with the JavaScript logic (e.g. references IFrames or IMG tags) is not possible. In most cases this should not be a problem, because usually GET requests should not facilitate state-changing operations. If this last condition cannot be fulfilled (e.g. for legacy applications), backwards compatibility can be achieved by enabling the "forceSynchronousAjax" property within the configurations, until there is browser support for it.
Aims to test token injection for referenced protected pages that are loaded before user interaction.
Aims to test token injection for referenced protected pages that are loaded before user interaction.


| Package | Description |
|---|---|
| org.apache.beam.runners.core | |
| org.apache.beam.runners.dataflow.options | |
| org.apache.beam.runners.dataflow.util | |
| org.apache.beam.runners.direct | |
| org.apache.beam.sdk.coders |
Defines
Coders
to specify how data is encoded to and decoded from byte strings. |
| org.apache.beam.sdk.coders.protobuf |
Defines a
Coder
for Protocol Buffers messages, ProtoCoder . |
| org.apache.beam.sdk.io |
Defines transforms for reading and writing common storage formats, including
AvroIO ,
BigQueryIO , and
TextIO . |
| org.apache.beam.sdk.io.hdfs | |
| org.apache.beam.sdk.options |
Defines
PipelineOptions for
configuring pipeline execution. |
| org.apache.beam.sdk.runners |
Defines runners for executing Pipelines in different modes, including
DirectPipelineRunner and
org.apache.beam.sdk.runners.DataflowPipelineRunner . |
| org.apache.beam.sdk.transforms |
Defines
PTransform s for transforming
data in a pipeline. |
| org.apache.beam.sdk.transforms.join |
Defines the
CoGroupByKey transform
for joining multiple PCollections. |
| org.apache.beam.sdk.transforms.windowing | |
| org.apache.beam.sdk.util |
Defines utilities used by the Dataflow SDK.
|
| org.apache.beam.sdk.util.state | |
| org.apache.beam.sdk.values |
Defines
PCollection and other classes for
representing data in a Pipeline . |
| Class and Description |
|---|
| DoFnRunner.ReduceFnExecutor
An internal interface for signaling that a
DoFn requires late data dropping. |
| SystemDoFnInternal
Annotation to mark
DoFns as an internal component of the Dataflow SDK. |
| Class and Description |
|---|
| PathValidator
Interface for controlling validation of paths.
|
| Class and Description |
|---|
| PathValidator
Interface for controlling validation of paths.
|
| WindowingStrategy
A
WindowingStrategy describes the windowing behavior for a specific collection of values. |
| Class and Description |
|---|
| DoFnRunners.OutputManager
Information about how to create output receivers and output to them.
|
TimeDomain
TimeDomain specifies whether an operation is based on
timestamps of elements or current "real-world" time as reported while processing. |
| TimerInternals
Encapsulate interaction with time within the execution environment.
|
| TimerInternals.TimerData
Data about a timer as represented within
TimerInternals . |
| WindowedValue
An immutable triple of value, timestamp, and windows.
|
| Class and Description |
|---|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| Class and Description |
|---|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| Class and Description |
|---|
| PubsubClient.ProjectPath
Path representing a cloud project id.
|
| PubsubClient.PubsubClientFactory
Factory for creating clients.
|
| PubsubClient.SubscriptionPath
Path representing a Pubsub subscription.
|
| PubsubClient.TopicPath
Path representing a Pubsub topic.
|
| Class and Description |
|---|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| Class and Description |
|---|
| CredentialFactory
Construct an oauth credential to be used by the SDK and the SDK workers.
|
| GcsUtil
Provides operations on GCS.
|
| Class and Description |
|---|
| GroupByKeyViaGroupByKeyOnly.GroupByKeyOnly
Runner-specific primitive that groups by key only, ignoring any window assignments.
|
| PerKeyCombineFnRunner
An interface that runs a
CombineFnBase.PerKeyCombineFn with unified APIs. |
| WindowedValue
An immutable triple of value, timestamp, and windows.
|
| Class and Description |
|---|
| AppliedCombineFn
A
CombineWithContext.KeyedCombineFnWithContext with a fixed accumulator coder. |
| WindowedValue
An immutable triple of value, timestamp, and windows.
|
| WindowingInternals
Interface that may be required by some (internal)
DoFn s to implement windowing. |
| WindowingStrategy
A
WindowingStrategy describes the windowing behavior for a specific collection of values. |
| Class and Description |
|---|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| Class and Description |
|---|
| ExecutableTrigger
A wrapper around a trigger used during execution.
|
TimeDomain
TimeDomain specifies whether an operation is based on
timestamps of elements or current "real-world" time as reported while processing. |
| WindowingStrategy
A
WindowingStrategy describes the windowing behavior for a specific collection of values. |
| Class and Description |
|---|
| ActiveWindowSet
Track which windows are active, and the state address window(s) under which their
state is stored.
|
| ActiveWindowSet.MergeCallback |
| ApiSurface
Represents the API surface of a package prefix.
|
| AppliedCombineFn
A
CombineWithContext.KeyedCombineFnWithContext with a fixed accumulator coder. |
| AttemptAndTimeBoundedExponentialBackOff.ResetPolicy
A ResetPolicy controls the behavior of this BackOff when reset() is called.
|
| AttemptBoundedExponentialBackOff
Implementation of
BackOff that increases the back off period for each retry attempt
using a randomization function that grows exponentially. |
| AvroUtils.AvroMetadata
Avro file metadata.
|
| BaseExecutionContext
Base class for implementations of
ExecutionContext . |
| BaseExecutionContext.CreateStepContextFunction
Factory method interface to create an execution context if none exists during
BaseExecutionContext.getOrCreateStepContext(String, CreateStepContextFunction) . |
| BaseExecutionContext.StepContext
Base class for implementations of
ExecutionContext.StepContext . |
| BigQueryServices
An interface for real, mock, or fake implementations of Cloud BigQuery services.
|
| BigQueryServices.BigQueryJsonReader
An interface to read the Cloud BigQuery directly.
|
| BigQueryServices.DatasetService
An interface to get, create and delete Cloud BigQuery datasets and tables.
|
| BigQueryServices.JobService
An interface for the Cloud BigQuery load service.
|
| BigQueryTableRowIterator
Iterates over all rows in a table.
|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| CredentialFactory
Construct an oauth credential to be used by the SDK and the SDK workers.
|
DirectModeExecutionContext
ExecutionContext for use in direct mode. |
DirectModeExecutionContext.StepContext
ExecutionContext.StepContext used in direct mode. |
| DirectSideInputReader
Basic side input reader wrapping a
PTuple of side input iterables. |
| DoFnRunner
An wrapper interface that represents the execution of a
DoFn . |
| DoFnRunner.ReduceFnExecutor
An internal interface for signaling that a
DoFn requires late data dropping. |
| DoFnRunnerBase
A base implementation of
DoFnRunner . |
| DoFnRunners.OutputManager
Information about how to create output receivers and output to them.
|
| ExecutableTrigger
A wrapper around a trigger used during execution.
|
| ExecutionContext
Context for the current execution.
|
| ExecutionContext.StepContext
Per-step, per-key context used for retrieving state.
|
| FinishedTriggers
A mutable set which tracks whether any particular
ExecutableTrigger is
finished. |
| FinishedTriggersBitSet
A
FinishedTriggers implementation based on an underlying BitSet . |
| FinishedTriggersSet
An implementation of
FinishedTriggers atop a user-provided mutable Set . |
| GatherAllPanes
Gathers all panes of each window into exactly one output.
|
| GcpCredentialFactory
Construct an oauth credential to be used by the SDK and the SDK workers.
|
| GcsUtil
Provides operations on GCS.
|
| GroupAlsoByWindowsDoFn
DoFn that merges windows and groups elements in those windows, optionally
combining values.
|
| InstanceBuilder
Utility for creating objects dynamically.
|
| IOChannelFactory
Defines a factory for working with read and write channels.
|
| KeyedWorkItem
Interface that contains all the timers and elements associated with a specific work item.
|
| KeyedWorkItemCoder
A
Coder for KeyedWorkItems . |
| MutationDetector
An object for detecting illegal mutations.
|
| NoopCredentialFactory
Construct an oauth credential to be used by the SDK and the SDK workers.
|
| NullSideInputReader
A
SideInputReader representing a well-defined set of views, but not storing
any values for them. |
| PathValidator
Interface for controlling validation of paths.
|
| PCollectionViewWindow
A pair of a
PCollectionView and a BoundedWindow , which can
be thought of as window "of" the view. |
| PerKeyCombineFnRunner
An interface that runs a
CombineFnBase.PerKeyCombineFn with unified APIs. |
| PTuple
A
PTuple is an immutable tuple of
heterogeneously-typed values, "keyed" by TupleTag s. |
| PubsubClient
An (abstract) helper class for talking to Pubsub via an underlying transport.
|
| PubsubClient.IncomingMessage
A message received from Pubsub.
|
| PubsubClient.OutgoingMessage
A message to be sent to Pubsub.
|
| PubsubClient.ProjectPath
Path representing a cloud project id.
|
| PubsubClient.PubsubClientFactory
Factory for creating clients.
|
| PubsubClient.SubscriptionPath
Path representing a Pubsub subscription.
|
| PubsubClient.TopicPath
Path representing a Pubsub topic.
|
| PubsubTestClient.PubsubTestClientFactory
Closing the factory will validate all expected messages were processed.
|
| PushbackSideInputDoFnRunner
A
DoFnRunner that can refuse to process elements that are not ready, instead returning
them via the PushbackSideInputDoFnRunner.processElementInReadyWindows(WindowedValue) . |
| ReadyCheckingSideInputReader
A
SideInputReader that allows callers to check to see if a PCollectionView has
had its contents set in a window. |
| ReduceFn
Specification for processing to happen after elements have been grouped by key.
|
| ReduceFn.Context
Information accessible to all the processing methods in this
ReduceFn . |
| ReduceFn.OnMergeContext
Information accessible within
ReduceFn.onMerge(org.apache.beam.sdk.util.ReduceFn<K, InputT, OutputT, W>.OnMergeContext) . |
| ReduceFn.OnTriggerContext
Information accessible within
ReduceFn.onTrigger(org.apache.beam.sdk.util.ReduceFn<K, InputT, OutputT, W>.OnTriggerContext) . |
| ReduceFn.ProcessValueContext
Information accessible within
ReduceFn.processValue(org.apache.beam.sdk.util.ReduceFn<K, InputT, OutputT, W>.ProcessValueContext) . |
| ReduceFnRunner
Manages the execution of a
ReduceFn after a GroupByKeyViaGroupByKeyOnly.GroupByKeyOnly has partitioned the
PCollection by key. |
| ReleaseInfo
Utilities for working with release information.
|
| Reshuffle
A
PTransform that returns a PCollection equivalent to its input but operationally
provides some of the side effects of a GroupByKey , in particular preventing fusion of
the surrounding transforms, checkpointing and deduplication by id (see
ValueWithRecordId ). |
| SideInputReader
The interface to objects that provide side inputs.
|
| SystemDoFnInternal
Annotation to mark
DoFns as an internal component of the Dataflow SDK. |
SystemReduceFn
ReduceFn implementing the default reduction behaviors of GroupByKey . |
TimeDomain
TimeDomain specifies whether an operation is based on
timestamps of elements or current "real-world" time as reported while processing. |
| TimerInternals
Encapsulate interaction with time within the execution environment.
|
| TimerInternals.TimerData
Data about a timer as represented within
TimerInternals . |
| TimerInternals.TimerDataCoder
A
Coder for TimerInternals.TimerData . |
| Timers
Interface for interacting with time.
|
| TriggerContextFactory
Factory for creating instances of the various
Trigger contexts. |
| UserCodeException
An exception that was thrown in user-code.
|
| ValueWithRecordId
Immutable struct containing a value as well as a unique id identifying the value.
|
| ValueWithRecordId.ValueWithRecordIdCoder |
| Weighted
Interface representing an object that has a weight, in unspecified units.
|
| WeightedValue
A
T with an accompanying weight. |
| WindowedValue
An immutable triple of value, timestamp, and windows.
|
| WindowedValue.FullWindowedValueCoder
Coder for
WindowedValue . |
| WindowedValue.ValueOnlyWindowedValueCoder
Coder for
WindowedValue . |
| WindowedValue.WindowedValueCoder
Abstract class for
WindowedValue coder. |
| WindowingInternals
Interface that may be required by some (internal)
DoFn s to implement windowing. |
| WindowingStrategy
A
WindowingStrategy describes the windowing behavior for a specific collection of values. |
| WindowingStrategy.AccumulationMode
The accumulation modes that can be used with windowing.
|
| Class and Description |
|---|
| WindowingInternals
Interface that may be required by some (internal)
DoFn s to implement windowing. |
| Class and Description |
|---|
| CloudObject
A representation of an arbitrary Java object to be instantiated by Dataflow
workers.
|
| WindowedValue
An immutable triple of value, timestamp, and windows.
|
| WindowingStrategy
A
WindowingStrategy describes the windowing behavior for a specific collection of values. |
Copyright © 2016 The Apache Software Foundation. All rights reserved.


mwarp1d¶
Manual one-dimensional data warping and nonlinear registration in Python and PyQt.
Source code is available in this project’s GitHub repository
mwarp1d provides a collection of GUI and scripting tools for manually warping 1D data, mainly for the purpose of nonlinear registration. As alternatives, a variety of algorithmic approaches to nonlinear 1D registration are available including:
Minimium eigenvalue (Ramsay & Silverman 2005)
Fisher-Rao square root velocity transform (Srivastava, Jermyn, & Joshi 2007)
K-mean alignment (Parodi et al. 2014)
Multi-level clusters (Wrobelm 2018)
Please cite:
Pataky TC, Naouma H, Donnelly CJ (in review). mwarp1d: Manual one-dimensional data warping in Python and PyQt. Journal of Open Source Software. Journal of Open Source Software.


| 23 Aug 1978 |
Kobe Bryant born, Philadelphia PA. He is named after a steak (his parents spotted the name on a menu, which actually refers to a city in Japan), and is the son of NBA basketball player Joe "Jellybean" Bryant. |
| 1984 |
Moved to Italy, where he lived until 1992. |
| 1996 |
Picked 13th in the NBA draft, by the Charlotte Hornets. Youngest player ever to play in an NBA game (at 18 years, 2 months, 11 days.) Quickly traded to the Lakers. |
| 30 Jun 2003 |
Rape allegedly occurs, Lodge & Spa, Edwards CO. |
| 1 Jul 2003 |
Accuser reports alleged rape to the police. |
| 2 Jul 2003 |
Provides DNA sample to Valley View Hospital. |
| 4 Jul 2003 |
Booked, suspicion of felony sexual assault, false imprisonment. |
| 18 Jul 2003 |
Charged with rape. |
| 9 Aug 2003 |
Appears in court before Judge Frederick Gannett. The town is a media circus. |


Rooftop View of Celebrations in German Capital to Mark the Fall of Berlin Wall - Video
Saturday marks the 30th anniversary of the fall of the wall separating Berlin, which had become one of the major symbols of Germany’s division and the Cold War.
On Saturday, people gathered in front of the Brandenburg Gate in the German capital to celebrate the dismantling of the wall that had seperated the city since 1961.
On the same day 30 years ago East Germans were granted access to travel abroad without restrictions and allowed to emigrate. This triggered a string of events that led to the eventual demolition of the barrier in June 1990 and the unification of East and West Germany in November of the same year.
FOLLOW OUR FEED TO FIND OUT MORE


#include <operation.hpp>
|
| | binary_operator (Lhs_Operator const &lhs_op, Rhs_Operator const &rhs_op, Forward_Action const &forward_action, Backward_Action const &backward_action, std::function< void()> const &reset_action) noexcept |
| |
| auto | forward () |
| |
| void | backward (tensor_type const &grad) |
| |
| void | reset_states () |
| |
◆ tensor_type
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ wrapped_lhs_operator_type
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ wrapped_rhs_operator_type
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ binary_operator()
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
| ceras::binary_operator< Lhs_Operator, Rhs_Operator, Forward_Action, Backward_Action >::binary_operator |
( |
Lhs_Operator const & |
lhs_op, |
|
|
Rhs_Operator const & |
rhs_op, |
|
|
Forward_Action const & |
forward_action, |
|
|
Backward_Action const & |
backward_action, |
|
|
std::function< void()> const & |
reset_action |
|
) |
| |
|
inlinenoexcept |
◆ backward()
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ forward()
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ reset_states()
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ backward_action_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
| Backward_Action ceras::binary_operator< Lhs_Operator, Rhs_Operator, Forward_Action, Backward_Action >::backward_action_ |
◆ forward_action_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
| Forward_Action ceras::binary_operator< Lhs_Operator, Rhs_Operator, Forward_Action, Backward_Action >::forward_action_ |
◆ lhs_input_data_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ lhs_op_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ output_data_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ reset_action_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
| std::function<void()> ceras::binary_operator< Lhs_Operator, Rhs_Operator, Forward_Action, Backward_Action >::reset_action_ |
◆ rhs_input_data_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
◆ rhs_op_
template<typename Lhs_Operator , typename Rhs_Operator , typename Forward_Action , typename Backward_Action >
The documentation for this struct was generated from the following file:
- /data/structured_folders/workspace/github.repo/ceras/include/operation.hpp


Class: Aws::Firehose::Types::DeliveryStreamDescription
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb |
Overview
Contains information about a delivery stream.
Constant Summary
Instance Attribute Summary
-
#create_timestamp ⇒ Time
rw
The date and time that the delivery stream was created.
-
#delivery_stream_arn ⇒ String
rw
The Amazon
::Aws::Firehose::Resource
Name (ARN) of the delivery stream. -
#delivery_stream_encryption_configuration ⇒ Types::DeliveryStreamEncryptionConfiguration
rw
Indicates the server-side encryption (SSE) status for the delivery stream.
-
#delivery_stream_name ⇒ String
rw
The name of the delivery stream.
-
#delivery_stream_status ⇒ String
rw
The status of the delivery stream.
-
#delivery_stream_type ⇒ String
rw
The delivery stream type.
-
#destinations ⇒ Array<Types::DestinationDescription>
rw
The destinations.
-
#failure_description ⇒ Types::FailureDescription
rw
Provides details in case one of the following operations fails due to an error related to KMS: CreateDeliveryStream, DeleteDeliveryStream, StartDeliveryStreamEncryption, StopDeliveryStreamEncryption.
-
#has_more_destinations ⇒ Boolean
rw
Indicates whether there are more destinations available to list.
-
#last_update_timestamp ⇒ Time
rw
The date and time that the delivery stream was last updated.
-
#source ⇒ Types::SourceDescription
rw
If the
DeliveryStreamType
parameter isKinesisStreamAsSource
, aSourceDescription
object describing the source::Aws::Kinesis
data stream. -
#version_id ⇒ String
rw
Each time the destination is updated for a delivery stream, the version ID is changed, and the current version ID is required when updating the destination.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#create_timestamp ⇒ Time
(rw)
The date and time that the delivery stream was created.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#delivery_stream_arn ⇒ String
(rw)
The Amazon ::Aws::Firehose::Resource
Name (ARN) of the delivery stream. For more
information, see Amazon ::Aws::Firehose::Resource
Names (ARNs) and Amazon Web
Services Service Namespaces.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#delivery_stream_encryption_configuration ⇒ Types::DeliveryStreamEncryptionConfiguration (rw)
Indicates the server-side encryption (SSE) status for the delivery stream.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#delivery_stream_name ⇒ String
(rw)
The name of the delivery stream.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#delivery_stream_status ⇒ String
(rw)
The status of the delivery stream. If the status of a delivery
stream is CREATING_FAILED
, this status doesn't change, and you
can't invoke CreateDeliveryStream
again on it. However, you can
invoke the DeleteDeliveryStream operation to delete it.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#delivery_stream_type ⇒ String
(rw)
The delivery stream type. This can be one of the following values:
DirectPut
: Provider applications access the delivery stream directly.KinesisStreamAsSource
: The delivery stream uses a::Aws::Kinesis
data stream as a source.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#destinations ⇒ Array
<Types::DestinationDescription> (rw)
The destinations.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#failure_description ⇒ Types::FailureDescription (rw)
Provides details in case one of the following operations fails due to an error related to KMS: CreateDeliveryStream, DeleteDeliveryStream, StartDeliveryStreamEncryption, StopDeliveryStreamEncryption.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#has_more_destinations ⇒ Boolean
(rw)
Indicates whether there are more destinations available to list.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#last_update_timestamp ⇒ Time
(rw)
The date and time that the delivery stream was last updated.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#source ⇒ Types::SourceDescription (rw)
If the DeliveryStreamType
parameter is KinesisStreamAsSource
, a
SourceDescription
object describing the source ::Aws::Kinesis
data stream.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end
#version_id ⇒ String
(rw)
Each time the destination is updated for a delivery stream, the version ID is changed, and the current version ID is required when updating the destination. This is so that the service knows it is applying the changes to the correct version of the delivery stream.
# File 'gems/aws-sdk-firehose/lib/aws-sdk-firehose/types.rb', line 961
class DeliveryStreamDescription < Struct.new( :delivery_stream_name, :delivery_stream_arn, :delivery_stream_status, :failure_description, :delivery_stream_encryption_configuration, :delivery_stream_type, :version_id, :, :, :source, :destinations, :has_more_destinations) SENSITIVE = [] include Aws::Structure end


Hi, I am Henrique Silverio! I am from São Paulo, Brazil. I am graduated in Business Administration and Management. I want to be a FrontEnd Developer. I am passionate about solving problems and learning. Always open to new challenges and ready to face changes.
National Basketball League (sport entity affiliated to National Basketball League)
Duration: from 03/2019 to 02/2020
Responsibilities:
Participation in projects with other departments
Participation in the development of the marketing platform
Preparation of management and sponsor reports
Support in the execution of sponsors' activities and events
Update of information and marketing data of League and clubs
If you want to know more about the projects I have already done, please click here and visit my GitHub profile!


{% extends 'template-lab-1b.html' %} {% set mainClasses = 'nhsuk-u-padding-top-0' %} {% block pageTitle %} Symptoms - NHS {% endblock %} {% block beforeContent %}
Pain in the bottom of your foot (ball, arch or sole) will often get better in a few weeks. See a GP if it does not improve.
If you see a GP, they'll usually suggest you try these things:
rest and raise your foot when you can
put an ice pack (or bag of frozen peas) in a towel on the painful area for up to 20 minutes every 2 to 3 hours
wear wide comfortable shoes with a low heel and soft sole
use soft insoles or pads you put in your shoes
try to lose weight if you're overweight
try regular gentle stretching exercises
take paracetamol
do not take ibuprofen for the first 48 hours after an injury
do not walk or stand for long periods
do not wear high heels or tight pointy shoes
These might be signs of a broken bone after an injury.
Pain in the bottom of your foot is often caused by exercising too much or wearing shoes that are too tight.
Your symptoms might also give you an idea of what's causing your pain.
| Symptoms | Possible cause |
|---|---|
| Pain, swelling, bruising, started after intense or repetitive exercise | sprained foot |
| Sharp, burning or shooting pain near your toes (ball of your foot), feels like a lump or small stone under your foot | Morton's neuroma |
| Sharp pain between your arch and heel, feels worse when you start walking and better when resting, difficulty raising toes off floor | plantar fasciitis |
| No gap (arch) under your foot when you stand up, your foot presses flat on the floor | flat feet (fallen arches) |
Do not worry if you're not sure what the problem is.
Follow the advice on this page and see a GP if the pain does not get better in 2 weeks.
You can also read about pain in other areas of your foot.
Page last reviewed: 02 August 2019
Next review due: 02 August 2022


Principal Investigator
SPHERES Lead Scientist, ZR Co-FounderDirector, Space Systems Laboratory
Project Manager
Systems Administrator
ZR Co-Founder
Undergraduate Researchers


It is not hard to see why such timescales are necessary. UTC would clearly be unsuitable as the argument of an ephemeris because of leap seconds. A solar-system ephemeris based on UT1 or sidereal time would somehow have to include the unpredictable variations of the Earth's rotation. TAI would work, but eventually the ephemeris and the ensemble of atomic clocks would drift apart. In effect, the ephemeris is a clock, with the bodies of the solar system the hands.
Only two of the dynamical timescales are of any great importance to observational astronomers, TT and TDB. (The obsolete timescale ET, ephemeris time, was more or less the same as TT.)
Terrestrial Time TT is the theoretical timescale of apparent geocentric ephemerides of solar system bodies. It applies, in principle, to an Earthbound clock, at sea-level, and for practical purposes it is tied to Atomic Time TAI through the formula TT = TAI + .In practice, therefore, the units of TT are ordinary SI seconds, and the offset of with respect to TAI is fixed. The SLALIB routine sla_DTT returns TT-UTC for a given UTC (n.b. sla_DTT calls sla_DAT, and the latter must be an up-to-date version if recent leap seconds are to be taken into account).
Barycentric Dynamical Time TDB differs from TT by an amount which cycles back and forth by a millisecond or two due to relativistic effects. The variation is negligible for most purposes, but unless taken into account would swamp long-term analysis of pulse arrival times from the millisecond pulsars. It is a consequence of the TT clock being on the Earth rather than in empty space: the ellipticity of the Earth's orbit means that the TT clock's speed and gravitational potential vary slightly during the course of the year, and as a consequence its rate as seen from an outside observer varies due to transverse Doppler effect and gravitational redshift. By definition, TDB and TT differ only by periodic terms, and the main effect is a sinusoidal variation of amplitude ; the largest planetary terms are nearly two orders of magnitude smaller. The SLALIB routine sla_RCC provides a model of TDB-TT accurate to a few nanoseconds. There are other dynamical timescales, not supported by SLALIB routines, which include allowance also for the secular terms. These timescales gain on TT and TDB by about /day.
For most purposes the more accessible TT is the timescale to use, for example when calling sla_PRENUT to generate a precession/nutation matrix or when calling sla_EVP to predict the Earth's position and velocity. For some purposes TDB is the correct timescale, for example when interrogating the JPL planetary ephemeris (see Starlink User Note 87), though in most cases TT will be near enough and will involve less computation.
Investigations of topocentric solar-system phenomena such as occultations and eclipses require solar time as well as dynamical time. TT/TDB/ET is all that is required in order to compute the geocentric circumstances, but if horizon coordinates or geocentric parallax are to be tackled UT is also needed. A rough estimate of is available via the routine sla_DT. For a given epoch (e.g. 1650) this returns an approximation to in seconds.
SLALIB --- Positional Astronomy Library


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Emotional
Our emotional reactions to painful topics can be intense. This can end up drawing focus onto you rather than the person who needs your support. Our tool allows you to process your emotions ahead of time so you can be more emotionally present in the moment.


Information Harmonization¶
This section addresses:
-
:USDA #4:
¶ Allow incorporation ARS ontology for ingredients nomenclature with access to external databases.
-
:USDA #8:
¶ Provide links to other nutrition-related databases.
Table of Contents¶
Ontomatica Coverage¶
Ontomatica harmonizes terms defined by the authorities below.
Our methods create relationships between terms by using one or more of the following predicates :
schema:sameAs
skos:exactMatch
skos:closeMatch
skos:relatedMatch
Harmonization enables a depositor to use the authority’s term, rather than an equivalent USDA term. Harmonization is essential for implementing USDA prime.
Authorities¶
| Domain | Harmonized Terms from Authorities | Example |
|---|---|---|
| Bio-chemistry | USDA ARS chemicals | |
| UN FAO INFOODS chemicals | ||
| US NIH PubChem chemicals | ||
| ChEBI chemicals | ||
| USDA NALT chemicals | ||
| USDA ARS GrainGenes (for wheat ontology) | ||
| UN FAO AgroVoc chemicals | ||
| Protein Data Bank (PDB) chemicals | ||
| Bio-chemical references in US Code of Federal Regulation | ||
| US NIH NCI Applied Research chemicals | ||
| US NIH NCI thesaurus chemicals | ||
| ChEMBL bioactive compounds | ||
| UniProt bioactive compounds | ||
| PDB bioactive compounds | ||
| US NIH NCBI Structure bioactive compounds | ||
| US NIH NCBI Taxon for bacteria and virus | ||
| US FDA Unique Ingredient Identifiers (UNII) | ||
| Enzyme Commission (EC) identifiers | ||
| DBpedia terms | ||
| Chemical Abstract Society (CAS) identifiers | ||
| Agricultural commodities | IR-4 commodities | |
| UN FAO AgroVoc commodities | ||
| UN FAO CODEX crop groups | ||
| UN FAO CODEX livestock groups | ||
| UN FAO fish commodities | ||
| USDA NALT commodities | ||
| US EPA Food Commodity Intake Data (FCID) | ||
| Agriculture commodity references in US Code of Federal Regulation | ||
| DBpedia terms | ||
| Food and meals | USDA ARS Food Patterns Equivalents Data (FPED) identities | |
| US NIH NCI Applied Research identities | ||
| Food identities in US Code of Federal Regulation | ||
| UN FAO food groups | ||
| Analytical methods | UN INFOODS tags for CODEX methods | |
| AOAC international methods | ||
| Methods associated with specific analytical devices and equipment |
Example: Fish¶
| Aquatic term | UN FAO term code | EUNIS term code | DBpedia term | AgroVoc term code | NALT term code |
|---|---|---|---|---|---|
| Common octopus | cl_species:OCC | eunis:60605 | dbpedia:Common_Octopus | agrovoc:c_5307 | nalt:55627 |
| Angler | cl_species:MON | eunis:124874 | dbpedia:Lophius_piscatorius | agrovoc:c_46042 | nalt:201339 |
| Red mullet | cl_species:MUT | eunis:124879 | dbpedia:Mullus_barbatus | agrovoc:c_43349 | nalt:43361 |
| Great Atlantic scallop | cl_species:SCE | eunis:60712 | dbpedia:Pecten_maximus | agrovoc:c_31159 | nalt:57135 |
| Red swamp crawfish | cl_species:RCW | eunis:258990 | dbpedia:Procambarus_clarkii | agrovoc:c_46824 | nalt:50664 |
| Swordfish | cl_species:SWO | eunis:124899 | dbpedia:Swordfish | agrovoc:c_7559 | nalt:65299 |
| Crested flounder | cl_species:LFG | dbpedia:Crested_flounder | agrovoc:c_45572 | nalt:40297 | |
| Giant grouper | cl_species:EEN | dbpedia:Giant_grouper | agrovoc:c_42341 | nalt:40299 | |
| Largemouth black bass | cl_species:MPS | eunis:10072 | dbpedia:Largemouth_bass | agrovoc:c_37035 | nalt:52789 |
| Pacific herring | cl_species:HEP | eunis:124939 | dbpedia:Pacific_herring | agrovoc:c_39009 | nalt:40303 |
| Pandalid shrimps | cl_species:PDZ | dbpedia:Pandalidae | agrovoc:c_46467 | nalt:31889 | |
| Penaeid shrimps | cl_species:PEZ | dbpedia:Penaeidae | agrovoc:c_46269 | nalt:31890 |
Example: Variety of Frequently Used Terms¶
| Term | UN FAO term code | NALT term code | DBpedia term |
|---|---|---|---|
| Brassica | agrovoc:c_1060 | nalt:19293 | dbpedia:Brassica |
| Flavonoids | agrovoc:c_2964 | nalt:10004 | dbpedia:Flavonoid |
| food composition | agrovoc:c_10961 | nalt:10481 | dbpedia:Food_composition_data |
| Bioavailability | agrovoc:c_32390 | nalt:10477 | dbpedia:Bioavailability |
| Nutritive value | agrovoc:c_5278 | nalt:10486 | dbpedia:Dietary_Reference_Values |
| Age | agrovoc:c_186 | nalt:110 | dbpedia:Ageing |
| Apples | agrovoc:c_541 | nalt:11308 | dbpedia:Apple |
| Processed foods | agrovoc:c_28228 | nalt:15553 | dbpedia:Processed_food |
| Carbonated beverages | agrovoc:c_28261 | nalt:16986 | dbpedia:Soft_drink |
| Fish products | agrovoc:c_2930 | nalt:9444 | dbpedia:Fish_products |
| Milk protein | agrovoc:c_4831 | nalt:9453 | dbpedia:Milk#Physical_and_chemical_structure |


Bluetooth speakers sold at Costco, Best Buy and Amazon recalled over impact hazard
by WFTS Digital StaffGrace Digital is recalling thousands of ECOXGEAR EcoBoulder Bluetooth Speakers over an impact hazard because the battery can overcharge and burst.
According to the recall, there have been five reports of speaker batteries bursting. That includes two reports of property damage to the surrounding area. There have not been any injuries reported.
| Model Number | UPC Code |
| GDI-EXBM900 (Orange) |<PHONE_NUMBER>31 |
| GDI-EXBM901 (Black) |<PHONE_NUMBER>48 |
| GDI-EXBM904 (Yellow) |<PHONE_NUMBER>96 |
| GDI-EXBM907 (Red) |<PHONE_NUMBER>02 |
| GDI-EXBM913 (Blue) |<PHONE_NUMBER>19 |
The speakers were sold nationwide at Costco from October 2016 to January 2017 and at Academy Sports, Cabela’s, Home Depot, Best Buy and Amazon from January 2018 through December 2018. They cost around $180.
RECALL ALERTS | The latest recalls that could affect you and your family
If you have one of the speakers, stop using it immediately and call ECOXGEAR to receive a free battery replacement kit.
ECOXGEAR can be reached at<PHONE_NUMBER> from 8 a.m. to 8 p.m. CT Monday through Friday, or email at<EMAIL_ADDRESS>or online at www.ecoxgear.com.


LETTER: Are the Californians moving to Nevada all liberal?
by Bruce Kester, HendersonWith all the rancor over Californians moving to Nevada and the Silver State’s liberal direction, I would love to see an interesting poll conducted. I would love to see a poll of the number of native-born liberals in our state versus the number of liberal California transplants.
Something tells me the percentage of native-born liberals accounts for the strong conservative voting history Nevada has always had. Now, of course, all three branches of our state government are headed as far left as California.


Relaxr
How I implemented Relaxr in 2 weeks and changed my life
My first big blob of text. My first big blob of text. My first big blob of text. My first big blob of text. My first big blob of text. My first big blob of text. My first big blob of text
Read More
I traveled across the globe thanks to Relaxr’s Excel auomation
Yet another blob of text. Yet another blob of text. Yet another blob of text. Yet another blob of text. Yet another blob of text. Yet another blob of text. Yet another blob of text
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The dashboard is a tool provided to the administrators, to edit the database values easily. The administrator can make new users, update user profiles, monitor and modify the activity of any user(related to their passwords and logs).
In this version of FOCUSA however, I decided to use the JavaScript console of the web browser for entering dashboard commands. It intially may be a little difficult to use, but it turns out to be a very time-saving decision on the long run. Reason being that an administrator can make use of all of the programming features of JavaScript while performing their daily jobs.
For example, let us assume that an administrator needs to register 25 new users to the FOCUSA database. These users may be referred by their roll numbers. In the case of a Graphical User Interface, the administrator would have to actually submit a addUser form 25 different times! However, this can be totally eliminated by using a powerful programming language like JavaScript. Below you will notice a surprising improvement over the GUI method, in this snippet of code below:
var students = "15CE"; for (var i=1;i<=25;i++) { addUser( students + i , "computerEngg" , "g"); //the syntax of this command is addUser(username , password , permissions) //registering 25 new users with username prefix 15CE, the same password for every account, and user priviledges of g. }
You can instantly notice the power of this illustration! I hope this justifies the reason why simplicity is always more powerful than the fancy Graphical User Interfaces.
Following is the set of commands currently supported by the Dashboard Console.
This function is created to register new users into the FOCUSA database.
This function can be used to update the abilities of a current user, modify their passwords, and escalate/demote their priviledges.
This function can be used to delete a certain user from FOCUSA.
This function can be used to retrieve user details from FOCUSA.
This function can be used to create a Topic(a category to post under). Topics created will appear in the post category choosing portion while posting. Remember that topic names require to follow a specific format. The name SHOULD have no spaces or punctuation(one word), and must begin and end with underscores. For example: _public_ or _news_
This function can be used to remove an exisiting Topic.
This function can be used to send a notification to any other user. The notification will appear in the other user's notification bar.
NOTE: You can always save the link as #nothing if you do not want the notification to get clicked.
Gives you a very powerful yet simple CLI to check the Logs. After typing this command, you can type searchLogs() to perform a search query on any Log you need to find.
Allows you to search the downloaded Logs very quickly, by just typing a regular expression based search query. Allows you to take advantage of the JS-RegExp as well. For example,
searchLogs("DASHBOARD");will return all dashboard related logs.
Deletes a conversation that is saved in the database, between two parties. For example,
deleteMessages('@admin','@admyn');will perform a delete of a conversation between the users @admin and @admyn. Note that it only deletes one side of the conversation, and to delete both sides, you will need to perform THE FOLLOWING:
deleteMessages('@admin','@admyn'); deleteMessages('@admyn','@admin');
Allows the admin who has logged in to logout via the console.