Currently, Apache Synapse does not have sophisticated support for modern tracing standardized. Therefore this new feature is intended to implement OpenTelemetery based tracing implementation for apache synapse.

This feature will include request-response training and inbound/outbound tracing at the transport level and the orchestration layer. Further, this also needs a really good investigation on Opentelemetry specification[1] and the Apache synapse transport component [1].

Relevant Skills

1. JAVA language
2. Understanding about observability 
3. Integration and Synapse configuration language.

[1]https://opentelemetry.io/ 
[2] http://synapse.apache.org/userguide/transports/pass_through.html

Apache ShardingSphere

Apache ShardingSphere is a distributed database middleware ecosystem, including 2 independent products, ShardingSphere JDBC and ShardingSphere Proxy presently. They all provide functions of data sharding, distributed transaction, and database orchestration.
Page: https://shardingsphere.apache.org
Github

Apache ShardingSphere

Apache ShardingSphere is a distributed database middleware ecosystem, including 2 independent products, ShardingSphere JDBC and ShardingSphere Proxy presently. They all provide functions of data sharding, distributed transaction, and database orchestration.
Page: https://shardingsphere.apache.org
Github: https://github.com/apache/shardingsphere

Background

ShardingSphere parser engine helps users parse a SQL to get the AST (Abstract Syntax Tree) and visit this tree to get SQLStatement (Java Object). At present, this parser engine can handle SQLs for `MySQL`, `PostgreSQL`, `SQLServer` and `Oracle`, which means we have to understand different database dialect SQLs.
More details: https://shardingsphere.apache.org/document/current/en/features/sharding/principle/parse/

Task

This issue is to proofread the DML(SELECT/UPDATE/DELETE/INSERT) SQL definitions for Oracle. As we have a basic Oracle SQL syntax definitions but do not keep in line with Oracle DOC, we need you to find out the vague SQL grammar definitions and correct them referring to Oracle DOC.

Notice, when you review these DML(SELECT/UPDATE/DELETE/INSERT) SQLs, you will find that these definitions will involve some basic elements of Oracle SQL. No doubt, these elements are included in this task as well.

Relevant Skills

1. Master JAVA language
2. Have a basic understanding of Antlr g4 file
3. Be familiar with Oracle SQLs

Targets files

1. DML SQLs g4 file: https://github.com/apache/shardingsphere/blob/master/shardingsphere-sql-parser/shardingsphere-sql-parser-dialect/shardingsphere-sql-parser-oracle/src/main/antlr4/imports/oracle/DMLStatement.g4
2. Basic elements g4 file: https://github.com/apache/shardingsphere/blob/master/shardingsphere-sql-parser/shardingsphere-sql-parser-dialect/shardingsphere-sql-parser-oracle/src/main/antlr4/imports/oracle/BaseRule.g4

References

1. Oracle SQL quick reference: https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlqr/SQL-Statements.html#GUID-1FA35EAD-AED2-4619-BFEE-348FF05D1F4A
2. Detailed Oracle SQL info: https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/19/sqlqr&id=SQLRF008

Mentor

Juan Pan, PMC of Apache ShardingSphere, panjuan@apache.org^{Image Removed}

Background

ShardingSphere parser engine helps users parse a SQL to get the AST (Abstract Syntax Tree) and visit this tree to get SQLStatement (Java Object). At present, this parser engine can handle SQLs for `MySQL`, `PostgreSQL`, `SQLServer` and `Oracle`, which means we have to understand different database dialect SQLs.
More details: https://shardingsphere.apache.org/document/current/en/features/sharding/principle/parse/

Task

This issue is to proofread the DML(SELECT/UPDATE/DELETE/INSERT) SQL definitions for Oracle. As we have a basic Oracle SQL syntax definitions but do not keep in line with Oracle DOC, we need you to find out the vague SQL grammar definitions and correct them referring to Oracle DOC.

Notice, when you review these DML(SELECT/UPDATE/DELETE/INSERT) SQLs, you will find that these definitions will involve some basic elements of Oracle SQL. No doubt, these elements are included in this task as well.

Relevant Skills

1. Master JAVA language
2. Have a basic understanding of Antlr g4 file
3. Be familiar with Oracle SQLs

Targets files

1. DML SQLs g4 file: https://github.com/apache/shardingsphere/blob/master/shardingsphere-sql-parser/shardingsphere-sql-parser-dialect/shardingsphere-sql-parser-oracle/src/main/antlr4/imports/oracle/DMLStatement.g4
2. Basic elements g4 file: https://github.com/apache/shardingsphere/blob/master/shardingsphere-sql-parser/shardingsphere-sql-parser-dialect/shardingsphere-sql-parser-oracle/src/main/antlr4/imports/oracle/BaseRule.g4

References

1. Oracle SQL quick reference: https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlqr/SQL-Statements.html#GUID-1FA35EAD-AED2-4619-BFEE-348FF05D1F4A
2. Detailed Oracle SQL info: https://docs.oracle.com/pls/topic/lookup?ctx=en/database/oracle/oracle-database/19/sqlqr&id=SQLRF008

Mentor

Juan Pan, PMC of Apache ShardingSphere, panjuan@apache.org^{Image Added}

Apache SkyWalking [1] is an application performance monitor (APM) tool for distributed systems, especially designed for microservices, cloud native and container-based (Docker, K8s, Mesos) architectures.

Tracing distributed systems is one of the main features of SkyWalking, with those traces, it can analyze some service metrics such as CPM, success rate, error rate, apdex, etc. SkyWalking also supports receiving metrics from the agent side directly.

In this task, we expect the Python agent to report its Python Virtual Machine (PVM) metrics, including (but not limited to, whatever metrics useful are also acceptable) CPU usage (%), memory used (MB), (active) thread/coroutine counts, garbage collection count, etc.

To complete this task, you must be comfortable with Python and gRPC, otherwise you'll have a hard time coming up to speed.

Live demo to play around: http://122.112.182.72:8080 (under reconstruction, maybe unavailable but latest demo address can be found at the GitHub index page http://github.com/apache/skywalking)

[1] http://skywalking.apache.org

Apache SkyWalking 

Apache SkyWalking [1] is an application performance monitor (APM) tool for distributed systems, especially designed for microservices, cloud native and container-based (Docker, K8s, Mesos) architectures.

SkyWalking is based on agent to instrument (automatically) monitored services, for now, we have many agents for different languages, Python agent [2] is one of them, which supports automatic instrumentations.

The goal of this project is to extend the agent's features by supporting profiling [3] a function's invocation stack, help the users to analyze which method costs the most major time in a cross-services call.

To complete this task, you must be comfortable with Python, have some knowledge of tracing system, otherwise you'll have a hard time coming up to speed..

[1] http://skywalking.apache.org
[2] http://github.com/apache/skywalking-python
[3] https://thenewstack.io/apache-skywalking-use-profiling-to-fix-the-blind-spot-of-distributed-tracing/

Background
Apache Traffic Control is a Content Delivery Network (CDN) control plane for large scale content distribution.

Traffic Control currently requires Apache Traffic Server as the underlying cache. Help us expand the scope by integrating with the very popular Varnish Cache.

There are multiple aspects to this project:

• Configuration Generation: Write software to build Varnish configuration files (VCL). This code will be implemented in our Traffic Ops and cache client side utilities, both written in Go.

• Health Monitoring: Implement monitoring of the Varnish cache health and performance. This code will run both in the Traffic Monitor component and within Varnish. Traffic Monitor is written in Go and Varnish is written in C.

• Testing: Adding automated tests for new code

Skills:

• Proficiency in Go is required
• A basic knowledge of HTTP and caching is preferred, but not required for this project.

Apache SkyWalking [1] is an application performance monitor (APM) tool for distributed systems, especially designed for microservices, cloud native and container-based (Docker, K8s, Mesos) architectures.

Tracing distributed systems is one of the main features of SkyWalking, with those traces, it can analyze some service metrics such as CPM, success rate, error rate, apdex, etc. SkyWalking also supports receiving metrics from the agent side directly.

In this task, we expect the Python agent to report its Python Virtual Machine (PVM) metrics, including (but not limited to, whatever metrics useful are also acceptable) CPU usage (%), memory used (MB), (active) thread/coroutine counts, garbage collection count, etc.

To complete this task, you must be comfortable with Python and gRPC, otherwise you'll have a hard time coming up to speed.

Live demo to play around: http://122.112.182.72:8080 (under reconstruction, maybe unavailable but latest demo address can be found at the GitHub index page http://github.com/apache/skywalking)

(More details to be added)

(More details to be added)