pgagroal: High-performance connection pool for PostgreSQL.
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Pedersen, Jesper
pgagroal is a high performance protocol-native connection pool for PostgreSQL. pgagroal is built upon libev, a high performance network library, and uses a shared-memory process model together with atomic operations for tracking state to achieve its high performance. In this session we will explore the * Architecture * Features * Deployment * Performance * Roadmap of pgagroal.
 Welcome to this presentation about PGAgral, a high-performance connection pool for Postgres. My name is Jasper Petersen and I work for Red Hat. In this talk we'll explore the architecture of PGAgral and thereby which use of visible features that can be implemented. We'll briefly look at how to do a deployment and the functionality of the management tools. Next we'll take a look at performance runs against other Postgres connection pools. And last we'll explore the roadmap of PGAgral and close with some thoughts. PGAgral is written in C and released under the 3-Clause PSD license. Let's start with the question. What is a connection pool? First, a connection pool must provide database connections to clients. Second, it provides a central access point to a database cluster. So a connection pool is an advanced proxy to one or more database instances and based on its features it can perform authentication pool connections in order to limit the overhead when a client obtains a connection and provide additional services around connection management. Of course, adding a connection pool adds an extra layer in the overall system architecture so its benefit needs to be weighted against the extra complexity and management. The overall architecture of PGAgral is process-based. This means that if a client crashes a connection it won't take down the entire pool as only the client process is terminated. Using a process model means that we need to have a shared memory model in order to maintain state across all processes. PGAgral uses LibEV for its network communication. LibEV is a fast network library that supports different event mechanisms. The state of PGAgral is maintained using atomic operations so a modern compiler is required to build the project. The Postgres native protocol is used for all communication which is implemented inside PGAgral so no dependencies is required for this. So the only dependencies are LibEV and OpenSSL for the runtime. This is an overall picture of the PGAgral architecture. On the left we have clients connecting to PGAgral that are implemented using different programming languages. They will interact with the security layer that defines how they will authenticate. Each client will be a process noted by C1 to C4 and the shared memory model will maintain state. PGAgral will communicate with Postgres using another security layer, will expand on these concepts in the following slides and look at the component breakdowns. First the shared memory structure is the central data structure for PGAgral. It is an in-map memory segment shared across all processes that contain the overall configuration and state of PGAgral. This includes the configuration settings from the deployment, the state of each of the connections done as an atomic assigned char, information about the Postgres service, limits and access control, known uses and the data structure for the connections. This structure gives all the processes a unified view of the runtime state of the entire system. For security we have two different layers, one layer towards Postgres and one layer for the connecting clients. In PGAgral we can configure authentication for the clients using different modules which are trust except without the password. We can reject the connection and then we have three different modes which requires a connection from the client. These are password which is plain text, MD5 and finally ScramShare which is the most secure method. Last, there is all which will use the same authentication mechanism as Postgres for the user database pair. So this setup provides us with the flexibility to for example use ScramShare for the clients but use MD5 or even trust when authenticating against Postgres. Password management is done based on a master key that is unique for all user stores. Each store uses symmetric encryption using EIS 256. Clients can communicate with PGAgral using the Chinesport layer security version 1.2, 1.3 for added security. All security functionality is implemented using the OpenSSL library version 1.0 or higher. During authentication we need a connection and this is where the pool component comes into play. The pool maintains the state of each of the connections. It provides an API to the rest of the system like getting a connection, returning a connection or even kiddling a connection. It also has methods for management operations. The connections are done as a flexible member array such that only the memory required for the specified maximum number of connections is allocated. This keeps the overall memory requirements for PGAgral down. But having a connection pool isn't enough as we may need to limit the number of connections available to a specific user or database. A limit rule will define a user, a database, a both and the number of available connections to the rule. The pool will choose the batch matching rule and allocate a connection based on that criteria. The number of active connections is maintained with an atomic unsigned short. This allows us to split the overall pool size into smaller sub pools and better control resources used for users and databases. For all of this to work we need to be able to communicate with both clients and Postgres itself using the version 3 of the Postgres protocol. PGAgral needs all message types during startup and when authenticating using the different authentication mechanisms. Each process has a fixed memory buffer which is used for communication. This means that we can allocate it in a static variable. The actual communication is done through an API that supports socket based communication or SSL based communication. Each client is a process and it follows the same steps. First it needs to authenticate where it will obtain a connection from the pool. If successful it will set up a pipeline instance which we'll come back to. Next is the interaction between the client and Postgres through PGAgral. Once done the connection is either returned to the pool or killed, lest the process exits. So what is a pipeline? The pipeline instance defines the behavior of the interaction of the client to PGAgral and from PGAgral to Postgres. There are currently two different pipelines in PGAgral. The first one is a performance pipeline which only looks for a terminate message from the client and a fatal error from Postgres. This makes the pipeline extremely fast as there is very little overhead. The second pipeline is a session pipeline which is like the performance one but also support TLS. The pipeline instance is triggered based on events from LeapEV. These events are created based on the status of the underlying network. There are different ways that these events are triggered, for example through select or E-Poll mechanisms. Last we have management of the pool. This is done over a Unix domain socket using a specific tool. The management layer allows the client process to send its socket descriptors to the parent process in order to transfer ownership. The management layer also allows for management operations to be implemented such as flush, disable a database, getting the status of the pool or performing shutdown. Now that we have gone through the architecture of PGAgral we can write a user visible feature set. PGAgral is of course a connection pool with trust password md5, mskymshia, security functionality. It supports authentication, pre-fill and pooling of connections. It can limit connection based on either database plus user, user only, database only or by the general pool. TLS is supported between clients and PGAgral. We can pre-fill the pool during startup and maintain a minimum number of connections based on a database and user pair. This requires a user vault to be specified. Idle connections can be removed after the specified number of seconds or turned off. In order to account for invalid connections we can perform connection validation either when a connection is obtained by the pool or in the background on idle connections. We can also turn this functionality off. Access to a database can be turned off and enabled again. And we can perform either graceful shutdown which allows all existing clients to finish or a fast shutdown that will do the shutdown at once. PGAgral can run in demon mode allowing it to be run in the background. User vaults are done using a master key and AES 256 symmetric encryption. And there is a runtime tool to control PGAgral and an administration tool to create the master key and the vaults. Here we can see the options to run PGAgral. There is the configuration file which contains how PGAgral is configured and how the Postgres instance is accessed. There is the host based access file which defines how client connect and which network mask are allowed. Next there is the definition of the limits for each database and user. This file is optional. Last there is the file that defines all the known users. This file is also optional. Let's do a simple configuration. Check our website for the full details of these files. The first file is pggral.conf which contains the main configuration. The main section is called pggral. We bind port 2, 3, 4, 5 on all network interfaces. We will do locking to our file at info level. There will be a maximum of 100 connections that has an idle timeout value of 10 minutes. Validation is off and we will specify the UNIX socket directory. Next we will define a section called primary which will be a local Postgres instance running on port 5, 4, 3, 2. The next file is the host based access file called pggral underscore hba.conf. We will let Alice connect using GramShare from all networks. Bob is restricted to the 10 network but will use MD5. All other users will use the same mechanism that Postgres will be using. The pggral underscore databases.conf shows that Alice and Bob both will have 5 connections prefittled each and they can use a maximum of 10 each. The remaining connections, 80 will be used for all other users. Now we need to generate the pggral underscore users.conf file. First we need to set the master key using the pggral admin tool. The master key must be 8 characters long, used at least one uppercase letter, at least one lowercase letter, at least one number and at least one special character. Once the master key is set we can add Alice and Bob with their passwords. We can now run pggral with the configurations file we just created. The log file shows that pggral has started. We can now use the Postgres client like psql to connect to pggral. Once Alice is done the connection will be put back into the pool. The pggral CLI tool provides the management operations like flushing the pool, enabling and disabling one or more databases. It also allows you to see the status of the pool and shut it down. The administration tool is pggral admin and it creates the master key and the user vaults. Now we'll take a look at the performance of pggral. The tests are run on Red Hat Enterprise Server 7.7 based machines on a 10G network. We'll compare pggral against three other connection pools which we'll call A, B and C. The same identifier will be used in all graphs. The version used were the latest on January 14, 2020 and all of them were configured with performance in mind. The tests were run using the pgbench tool. But please run your own benchmarks. The first graph is pgbench using the prepared mode. The x-axis is the number of clients and the y-axis is the number of transactions per second. The second graph is pgbench with the prepared and read only mode. Again the x-axis is the number of clients and the y-axis is the number of transactions per second. The first thing that is important to note is the increase in transactions per second compared to the other pools which means that pggral can drive more load towards postgres. The second thing that is important is that pggral can reach transactions per second value a lot sooner than the other pools. This could mean that a much smaller machine can be used for the pggral deployment in order to drive the same load. Looking at performance in general, pggral will use around 5MB of RSS memory and the overhead of each connection is around 67KB. This means that pggral has a very small memory footprint. Due to the libEV event mechanism and the fixed network buffer, there are basically zero allocations at runtime in pggral itself. This means that pggral is very cloud friendly as it uses a limited number of resources to manage the connections and it scales well using the CPU resources available. In the future there will be an IOU ring module for libEV which should make pggral faster. This will require a Linux 5.6 or later kernel. The next release of pggral will be 0.7 which will have Prometheus support for monitoring and remote management such that administrator doesn't have to log into the machine. Of course there will be other improvements and box fixes as well. Check the release notes once it is out. The roadmap for pggral is maintained on our github account and contains features like failover support in order to promote a replica instance to a primary instance, a high mobility setup where multiple instances of pggral work together as one pool, support for running selects on replicas if the transaction is read only, a transaction pipeline that returns the connection after each transaction has ended, and a query cache. Check the issues tab and vote. So if you found this talk interesting feel free to try out pggral yourself and do your own performance benchmark in order to see how it compares to existing connection pool deployment. If you like what you see give the project a star on github and follow us on twitter. You can vote for features or create a new feature request if something is missing. And of course contribute. Every contribution to the project is most welcome as we move towards building an advanced connection pool implementation for Postgres. Thank you for your time and hope to see you in the pggral community. And we're live now for a Q&A session with Jesper. Jesper you please go ahead. Thank you. Yeah so there was a couple of questions during my session about pggral and the first one was what the major differences was between pggral and the pools out there. And I would say that one of the major differences is that pggral is process based which basically takes a lot of the architecture is done in turn. Then there was a question about pooling modes pggral currently has and currently we have session pooling but traction pooling is on the roadmap. Then there was a question about let's see if the command rules could be scripted to be scraped for monitoring jobs such as permissed and actually the seven release just went out yesterday which has native Prometheus support on its own internal server. The sea pool has a human readable format course both in brief mode and in more else. And then the question about why pggral can't do select and replicate moment is currently on roadmap but there is definitely not to consider around this functionality. Have to have a read only action but we also have to set a stuff like location lack and the parameters around that based on the you put. And I think that was it for the questions. Okay. If that's all the questions then we're done. Thank you very much. I appreciate your help. Stan, thanks for hosting. Oh, you're welcome. My pleasure. Goodbye. Thank you.
 Welcome to this presentation about PGAgral, a high-performance connection pool for Postgres.  My name is Jasper Petersen and I work for Red Hat.  In this talk we'll explore the architecture of PGAgral and thereby which use of visible  features that can be implemented.  We'll briefly look at how to do a deployment and the functionality of the management tools.  Next we'll take a look at performance runs against other Postgres connection pools.  And last we'll explore the roadmap of PGAgral and close with some thoughts.  PGAgral is written in C and released under the 3-Clause PSD license.  Let's start with the question.  What is a connection pool?  First, a connection pool must provide database connections to clients.  Second, it provides a central access point to a database cluster.  So a connection pool is an advanced proxy to one or more database instances and based  on its features it can perform authentication pool connections in order to limit the overhead  when a client obtains a connection and provide additional services around connection management.  Of course, adding a connection pool adds an extra layer in the overall system architecture  so its benefit needs to be weighted against the extra complexity and management.  The overall architecture of PGAgral is process-based.  This means that if a client crashes a connection it won't take down the entire pool as only  the client process is terminated.  Using a process model means that we need to have a shared memory model in order to maintain  state across all processes.  PGAgral uses LibEV for its network communication.  LibEV is a fast network library that supports different event mechanisms.  The state of PGAgral is maintained using atomic operations so a modern compiler is required  to build the project.  The Postgres native protocol is used for all communication which is implemented inside  PGAgral so no dependencies is required for this.  So the only dependencies are LibEV and OpenSSL for the runtime.  This is an overall picture of the PGAgral architecture.  On the left we have clients connecting to PGAgral that are implemented using different programming  languages.  They will interact with the security layer that defines how they will authenticate.  Each client will be a process noted by C1 to C4 and the shared memory model will maintain  state.  PGAgral will communicate with Postgres using another security layer, will expand on these  concepts in the following slides and look at the component breakdowns.  First the shared memory structure is the central data structure for PGAgral.  It is an in-map memory segment shared across all processes that contain the overall configuration  and state of PGAgral.  This includes the configuration settings from the deployment, the state of each of the connections  done as an atomic assigned char, information about the Postgres service, limits and access  control, known uses and the data structure for the connections.  This structure gives all the processes a unified view of the runtime state of the entire system.  For security we have two different layers, one layer towards Postgres and one layer for  the connecting clients.  In PGAgral we can configure authentication for the clients using different modules which  are trust except without the password.  We can reject the connection and then we have three different modes which requires a connection  from the client.  These are password which is plain text, MD5 and finally ScramShare which is the most secure  method.  Last, there is all which will use the same authentication mechanism as Postgres for the  user database pair.  So this setup provides us with the flexibility to for example use ScramShare for the clients  but use MD5 or even trust when authenticating against Postgres.  Password management is done based on a master key that is unique for all user stores.  Each store uses symmetric encryption using EIS 256.  Clients can communicate with PGAgral using the Chinesport layer security version 1.2,  1.3 for added security.  All security functionality is implemented using the OpenSSL library version 1.0 or higher.  During authentication we need a connection and this is where the pool component comes  into play.  The pool maintains the state of each of the connections.  It provides an API to the rest of the system like getting a connection, returning a connection  or even kiddling a connection.  It also has methods for management operations.  The connections are done as a flexible member array such that only the memory required for  the specified maximum number of connections is allocated.  This keeps the overall memory requirements for PGAgral down.  But having a connection pool isn't enough as we may need to limit the number of connections  available to a specific user or database.  A limit rule will define a user, a database, a both and the number of available connections  to the rule.  The pool will choose the batch matching rule and allocate a connection based on that criteria.  The number of active connections is maintained with an atomic unsigned short.  This allows us to split the overall pool size into smaller sub pools and better control  resources used for users and databases.  For all of this to work we need to be able to communicate with both clients and Postgres  itself using the version 3 of the Postgres protocol.  PGAgral needs all message types during startup and when authenticating using the different  authentication mechanisms.  Each process has a fixed memory buffer which is used for communication.  This means that we can allocate it in a static variable.  The actual communication is done through an API that supports socket based communication  or SSL based communication.  Each client is a process and it follows the same steps.  First it needs to authenticate where it will obtain a connection from the pool.  If successful it will set up a pipeline instance which we'll come back to.  Next is the interaction between the client and Postgres through PGAgral.  Once done the connection is either returned to the pool or killed, lest the process exits.  So what is a pipeline?  The pipeline instance defines the behavior of the interaction of the client to PGAgral  and from PGAgral to Postgres.  There are currently two different pipelines in PGAgral.  The first one is a performance pipeline which only looks for a terminate message from the  client and a fatal error from Postgres.  This makes the pipeline extremely fast as there is very little overhead.  The second pipeline is a session pipeline which is like the performance one but also  support TLS.  The pipeline instance is triggered based on events from LeapEV.  These events are created based on the status of the underlying network.  There are different ways that these events are triggered, for example through select  or E-Poll mechanisms.  Last we have management of the pool.  This is done over a Unix domain socket using a specific tool.  The management layer allows the client process to send its socket descriptors to the parent  process in order to transfer ownership.  The management layer also allows for management operations to be implemented such as flush,  disable a database, getting the status of the pool or performing shutdown.  Now that we have gone through the architecture of PGAgral we can write a user visible feature  set.  PGAgral is of course a connection pool with trust password md5, mskymshia, security functionality.  It supports authentication, pre-fill and pooling of connections.  It can limit connection based on either database plus user, user only, database only or by  the general pool.  TLS is supported between clients and PGAgral.  We can pre-fill the pool during startup and maintain a minimum number of connections based  on a database and user pair.  This requires a user vault to be specified.  Idle connections can be removed after the specified number of seconds or turned off.  In order to account for invalid connections we can perform connection validation either  when a connection is obtained by the pool or in the background on idle connections.  We can also turn this functionality off.  Access to a database can be turned off and enabled again.  And we can perform either graceful shutdown which allows all existing clients to finish  or a fast shutdown that will do the shutdown at once.  PGAgral can run in demon mode allowing it to be run in the background.  User vaults are done using a master key and AES 256 symmetric encryption.  And there is a runtime tool to control PGAgral and an administration tool to create the master  key and the vaults.  Here we can see the options to run PGAgral.  There is the configuration file which contains how PGAgral is configured and how the Postgres  instance is accessed.  There is the host based access file which defines how client connect and which network  mask are allowed.  Next there is the definition of the limits for each database and user.  This file is optional.  Last there is the file that defines all the known users.  This file is also optional.  Let's do a simple configuration.  Check our website for the full details of these files.  The first file is pggral.conf which contains the main configuration.  The main section is called pggral.  We bind port 2, 3, 4, 5 on all network interfaces.  We will do locking to our file at info level.  There will be a maximum of 100 connections that has an idle timeout value of 10 minutes.  Validation is off and we will specify the UNIX socket directory.  Next we will define a section called primary which will be a local Postgres instance running  on port 5, 4, 3, 2.  The next file is the host based access file called pggral underscore hba.conf.  We will let Alice connect using GramShare from all networks.  Bob is restricted to the 10 network but will use MD5.  All other users will use the same mechanism that Postgres will be using.  The pggral underscore databases.conf shows that Alice and Bob both will have 5 connections  prefittled each and they can use a maximum of 10 each.  The remaining connections, 80 will be used for all other users.  Now we need to generate the pggral underscore users.conf file.  First we need to set the master key using the pggral admin tool.  The master key must be 8 characters long, used at least one uppercase letter, at least  one lowercase letter, at least one number and at least one special character.  Once the master key is set we can add Alice and Bob with their passwords.  We can now run pggral with the configurations file we just created.  The log file shows that pggral has started.  We can now use the Postgres client like psql to connect to pggral.  Once Alice is done the connection will be put back into the pool.  The pggral CLI tool provides the management operations like flushing the pool, enabling  and disabling one or more databases.  It also allows you to see the status of the pool and shut it down.  The administration tool is pggral admin and it creates the master key and the user vaults.  Now we'll take a look at the performance of pggral.  The tests are run on Red Hat Enterprise Server 7.7 based machines on a 10G network.  We'll compare pggral against three other connection pools which we'll call A, B and  C. The same identifier will be used in all graphs.  The version used were the latest on January 14, 2020 and all of them were configured with  performance in mind.  The tests were run using the pgbench tool.  But please run your own benchmarks.  The first graph is pgbench using the prepared mode.  The x-axis is the number of clients and the y-axis is the number of transactions per second.  The second graph is pgbench with the prepared and read only mode.  Again the x-axis is the number of clients and the y-axis is the number of transactions per second.  The first thing that is important to note is the increase in transactions per second  compared to the other pools which means that pggral can drive more load towards postgres.  The second thing that is important is that pggral can reach transactions per second value  a lot sooner than the other pools.  This could mean that a much smaller machine can be used for the pggral deployment in order  to drive the same load.  Looking at performance in general, pggral will use around 5MB of RSS memory and the  overhead of each connection is around 67KB.  This means that pggral has a very small memory footprint.  Due to the libEV event mechanism and the fixed network buffer, there are basically zero allocations  at runtime in pggral itself.  This means that pggral is very cloud friendly as it uses a limited number of resources to  manage the connections and it scales well using the CPU resources available.  In the future there will be an IOU ring module for libEV which should make pggral faster.  This will require a Linux 5.6 or later kernel.  The next release of pggral will be 0.7 which will have Prometheus support for monitoring  and remote management such that administrator doesn't have to log into the machine.  Of course there will be other improvements and box fixes as well.  Check the release notes once it is out.  The roadmap for pggral is maintained on our github account and contains features like  failover support in order to promote a replica instance to a primary instance, a high mobility  setup where multiple instances of pggral work together as one pool, support for running  selects on replicas if the transaction is read only, a transaction pipeline that returns  the connection after each transaction has ended, and a query cache.  Check the issues tab and vote.  So if you found this talk interesting feel free to try out pggral yourself and do your  own performance benchmark in order to see how it compares to existing connection pool  deployment.  If you like what you see give the project a star on github and follow us on twitter.  You can vote for features or create a new feature request if something is missing.  And of course contribute.  Every contribution to the project is most welcome as we move towards building an advanced  connection pool implementation for Postgres.  Thank you for your time and hope to see you in the pggral community.  And we're live now for a Q&A session with Jesper.  Jesper you please go ahead.  Thank you.  Yeah so there was a couple of questions during my session about pggral and the first one  was what the major differences was between pggral and the pools out there.  And I would say that one of the major differences is that pggral is process based which basically  takes a lot of the architecture is done in turn.  Then there was a question about pooling modes pggral currently has and currently we have  session pooling but traction pooling is on the roadmap.  Then there was a question about let's see if the command rules could be scripted to be  scraped for monitoring jobs such as permissed and actually the seven release just went  out yesterday which has native Prometheus support on its own internal server.  The sea pool has a human readable format course both in brief mode and in more else.  And then the question about why pggral can't do select and replicate moment is currently  on roadmap but there is definitely not to consider around this functionality.  Have to have a read only action but we also have to set a stuff like location lack and  the parameters around that based on the you put.  And I think that was it for the questions.  Okay.  If that's all the questions then we're done.  Thank you very much.  I appreciate your help.  Stan, thanks for hosting.  Oh, you're welcome.  My pleasure.  Goodbye.  Thank you.
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